{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:45.728977Z",
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     "shell.execute_reply": "2025-02-07T13:04:45.731324Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.728949Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:45.732841Z",
     "iopub.status.busy": "2025-02-07T13:04:45.732592Z",
     "iopub.status.idle": "2025-02-07T13:04:45.741179Z",
     "shell.execute_reply": "2025-02-07T13:04:45.740494Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.732821Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:45.742145Z",
     "iopub.status.busy": "2025-02-07T13:04:45.741813Z",
     "iopub.status.idle": "2025-02-07T13:04:45.744753Z",
     "shell.execute_reply": "2025-02-07T13:04:45.744122Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.742125Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T13:04:45.771219Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.745695Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1128154.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:45.773349Z",
     "iopub.status.busy": "2025-02-07T13:04:45.773020Z",
     "iopub.status.idle": "2025-02-07T13:04:45.782063Z",
     "shell.execute_reply": "2025-02-07T13:04:45.781384Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.773314Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-07T13:04:45.785062Z",
     "iopub.status.idle": "2025-02-07T13:04:45.787713Z",
     "shell.execute_reply": "2025-02-07T13:04:45.787089Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.785474Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
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     "iopub.status.idle": "2025-02-07T13:04:45.794805Z",
     "shell.execute_reply": "2025-02-07T13:04:45.794210Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.788784Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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LxYJJkybhH//4B9q3b4/WrVvjgQceQGpqKoYNGxbMoRNCCCEkSAR18nL22WfjnXfewZQpUzBz5ky0bt0as2fPxsiRI1Wde+65B8XFxRg/fjzy8/MxYMAALF26FG63O4gjJ4QQQmoHA3YDJ+i5jS699FJceumlfo9bLBbMnDkTM2fOrNV5Jsf9hugoG16454+q7G9LrlN68w0SJLxokbiF9vQVJxAAvPNZutKPjZQ8SQe9R5WOH7hX6d2/iFMq6t8HlW66Ttwxq7RcPtckS56j/2tzienccb+K82VrpbhzCtPkAxx1uEDpAx5JBNSymZzbsUecQJsrYrUTiPvKvV/6B4DmMZLHqTAyAr7wxsq92lMisUYJmovGeUSsIZqEUSlOoIJSyckEAE0d8jG1y22Gxy2uG0e5nNujuY1gyLndTi2XkpbbKMqmWZhOcBu5tRxBXoeeC0hzu/j5LdJzG3m0cVg1t5FHs8o4bPL+AmZnjzm3kZZjSXdD+cl5pLuQbNDdRpo76aS8Sn5yG0G/B/CJfm/8UVWeIn/moZq7ioJIQxprI8dSj98LD6ym3+eatw9dgj55IYQQQkIRo5ZxK4EkOG0sBD23ESGEEEJITeDKCyGEEBIEGPMSOJy8EEIIIUHAY1hNMWk1b1+Hg2lg8LERIYQQQhoUIbPyMmJrJhwRTqzMnK3Kbhk0Wul/DOmkdNRHPyh9/ZgbTf2kvS/OlKVXSY6gr450VXpmu3eVXhj1B6UPJDWVjn6WHETz956n9NxWS5Se01VyDQFAwrp8pVcUS26k4taaI0pztWyukJxH5zTZrvQPeTLu70tbKl2ZIOdzHJR8RADQIVJ2Of42qr3SRw1xJYXFyL3ZVyR9RVrkY+Yokvj4Q5otyKgQV09RiW4XApo6HUrbj2pOHbc29y6T/EQeuTwTYQ65TxbNbRRulWswHGa3kUPPBWT3nXPHa24ifdl95zCy2rQ+tfp63iHAvCTs8JfDyE+5yyLXqjuE9JxHVS05e2v4bbCmOY90quNOqhI/3z4DCmb01yaEv+GS04cXFnhrsYbgDeEPZshMXgghhJD6BGNeAoePjQghhBDSoODKCyGEEBIEah+wy8dGhBBCCDmDHIt5CfzRT23aNnT42IgQQgghDYqQWXkpeKY57A43CubIJXt+2670G69foHQLxwalLW80MfVj+/pbpSd/d43SZQckH8+Dl/+kdFTKZ0rfefYEpV0frFU653vJl9S0jbh0DomBCQCQsERyJn24r5uMt9UBGV+ktF9ztK3S50RuUfrHg4ly7oJWSpcmissncluu6dzt3OI2WhvbU+kjXnG1JEUfUXrnPsmf5NLcRvZicRUd9EqOJMMjLqTyErNdyOLQ3Ubirql0aXPvCs1d4/a9lBruEEcSTG4jKT/ZbaQ5hhy6a6cauY38fDXQ3UYezS3gPCG3kZ7zRM9t5PWT26hCS6Zks+j5k7TcRpo7qVKzSdlgdjrp7iGrxXe5jj9nj7/yqrZEr2lfNSZ0V9pJPcNby9xGoew24soLIYQQEgSOx7zU5hUIc+fORVpaGtxuN9LT07F27Vq/dc8//3xYLJaTXkOHDlV1brzxxpOODxkyJKCxVZeQWXkhhBBC6hNeWM/4Pi9vvPEGsrKyMG/ePKSnp2P27NkYPHgwNm3ahKZNm55U/+2330Z5uaxOHzx4ED169MDVV19tqjdkyBAsXLhQ/exymffrqmu48kIIIYSECE8++STGjRuHMWPGoEuXLpg3bx7Cw8OxYMECn/Xj4+ORnJysXsuWLUN4ePhJkxeXy2WqFxcXd1qvg5MXQgghJAh4DEutXwBQWFhoepWVlfk8X3l5OXJycpCZmanKrFYrMjMzkZ2dXa0xz58/HyNGjEBERISpfOXKlWjatCk6duyIW265BQcPHgzwrlSPkHls5P4oB3aLA8MGTVJlqcPkeMuFm5U+MKyL0glLfjZ3FCFb7se8J8Gx+rb1Wy8pUrqPU+rszZDb3XallCesk8DJw1eUSHkXCcQFAE9+vtKbt8oW/X/uKx+6b5OkPPuwBBFf0fJHpb0FElj784E0pW1NZRwRR+QaAKCNY5/SZQlupQ95JYiyRaSMb/uWZKUdesBukSw/5lbEygm0tAYoPmG/fZcE8OoBu6UJ0q9RrgXsOs3Bp8eJ1AJ2S+3S1m2VtobdPJ93aMuyenoAm0VLD+Dvt8iUHkDGZLPqW/pr1S3+g2b1wNxyPTDXtN2/HsgrgdEVfurrNkvbCcvP/iyY+phsWh2/6QH8Bt/6LK41+vtyJrA0oHjJgMbagK6vIeKpZcDu8YD/Fi1amMqnTZuG6dOnn1T/wIED8Hg8SEpKMpUnJSXhl19+OeX51q5di59++gnz5883lQ8ZMgRXXXUVWrduja1bt+Jvf/sbLr74YmRnZ8Nm85M/pZaEzOSFEEIIaYzs2rUL0dHR6ufTFW8yf/58dOvWDf369TOVjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrHwsREhhBASBLyGtdYvAIiOjja9/E1emjRpApvNhry8PFN5Xl4ekpOTfbY5TnFxMRYvXoyxY8ee8rratGmDJk2aYMuWLaesGyicvBBCCCFB4Phjo9q8aoLT6USfPn2wfPlyVeb1erF8+XJkZGRU2fbNN99EWVkZbrjhhlOe5/fff8fBgweRkpJSo/HVBE5eCCGEkBAhKysLL730El599VVs3LgRt9xyC4qLizFmzBgAwKhRozBlypST2s2fPx/Dhg1DQkKCqbyoqAh33303Vq9eje3bt2P58uW44oor0K5dOwwePPi0XQdjXgghhJAg4AWUYyjQ9jXl2muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW87rGpk2b8OWXX+LTTz89qT+bzYYffvgBr776KvLz85GamoqLLroIDz744Gnd6yVkJi/lmb3hdbjR6Z+/q7LCl8TFYnxyVGnbiP1S/qY4UQCgaIhsy5/wyVY5oL3ZM3dfovSjzT9SuuU5u6TfDrItf5PvCpT+slTSEfyp5Xemc39mi1E64lfZMv+88yVK/KuWkmrgt1xxNDVvLR8ib1mp0vm5EuQVIVkD4D0q9wMAmtmLlS6Ll4/N7kppnxYu1rjsQt+LetYScfzkaW11bCUntNV+AexHxXVT6ZZ7gEpx1xgu37/S4XbdbSTvfYRVbIWG3RwZ77RojhoHfOI3PYBN36JftF1LD6CP9MT0ALqryKE5lLyGP1eRDETf7t+f46eqP5pmV5Hhs1xH37rf6sep5K/85M6qV83XuWvTzxmhqjH5O1Yfr4PUCbXfpC6wthMnTsTEiRN9Hlu5cuVJZR07doThxyIYFhaGTz75JKBx1AY+NiKEEEJIgyJkVl4IIYSQ+kRt8hMdbx+qcPJCCCGEBAEvLH43hKxu+1CFkxdCCCEkCHDlJXBC98oJIYQQ0iAJmZUX+x15sEe4YFwleX3eO2up0hdef6fSb5z1uNKjLpNyANh7sbg7wt/Rcg9puXm+/p9s9rPw4lyl70wTm9n9fWSXwqb//kHpxftk2+XpzT4wnfvzJlcqHferOFN6ugqVLmwtLpqK38Ue47L4tsq498pHoDRRoskNj9n5Em+VekcTZM67q0I8/61d4tJyHNFcOrqnpkRcTHtK9ayj4gSyl5iXQg3NVWQ7Kve/0q3VqZByq1Mbu5brJsouLqtDdslR5bbIub3OE3Ib6W4ju+6o0XIb+UndYbHrriLfbiOPtux7Um4j7Rx2LbeRvjGV1aI7mvTcS3oOI60+fLuWrCckvqlOrqLqfPPxl8PIr0Ooys5O/xL56cq5RIgvap/bKHTXH0Jm8kIIIYTUJ7yGxe8Xheq2D1VCd9pGCCGEkAYJV14IIYSQIOCt5WOj2mxw19Dh5IUQQggJAnpm6EDbhyqhe+WEEEIIaZCEzMrLOx0/RnSUFd1um6DK9njEbXTWmA1Kp9rE3VI4QtxJADC182dKv9ErU2nrQXH8tFguzpcFzfsr/fMff1b61t7iHkl4qUjp7F/7Kt02TRwxAFDZWtKLR26RfEgJ1ggZb5oEcIXvEl1mSI4ma1iY0mF50n9FW3HjWGxmC02kVfILlcZL+bYySYh0XqTkWHLK7TCdG0flHLmlktvIYjustJZGCQDgDZP3w1Iq99aj5fzS3VF2l2j9OiK13EZwSJ/hutvIcYLbSHcV+cthpJWbnFVWzQmku41MeYqglZsdXroTyaG5jSq0nEdOLbdRgSHvq+4qMruQfOc8ssG/20i/I/5cQtXJeVRdatympg4hrb61ut/fajymMxBIGcLBmo0FDyym3/NA2ocqITN5IYQQQuoTfGwUOKF75YQQQghpkHDlhRBCCAkCHtTu0Y/n1FUaLZy8EEIIIUGAj40CJ2QmLy8VtITbY8f0UYtU2RVf3qr0xvNfVnrC7guUXtRrvqmfs5wSJfro5TFKR2+T4NOEJRKYG9n+LKXLzpPgyrO7bVW6KLGJ1P9J297/QvO8uqC9BPDGv79DaT1ItKK1BMQmfCB9/e4pU9oaK+OO3CvnCGsiUbZ6UC9gDmwsT5DzbS+R9ADXxUp7Z6FERR7xSsCuUSrj2H9UIn9ddglatpeYTg2PWz6mzoKjWrnU0QN2nU65zxarfKuJtMm9gV2CXt1Wqa+nAAAAq/atyOs7wwK8dt8Ro1Y/6QEcNhlruSkFgDk9gB6Yqwfa6snYrFq/+h+y6gTmVpWR1m96AD9t9G31bdX4JmmYAoL9/wHW73/Nt+4P3WBG0jBgYsbACd0rJ4QQQkiDJGRWXgghhJD6hAFLlSug1WkfqnDyQgghhAQBPjYKnNC9ckIIIYQ0SLjyQgghhAQBr2HxGxxf3fahSshMXl5bOBg2lxvZd89WZS++IMeXnyNOnuz/9lB67u1fmPr5tULcLv0GS0qBLze3VTr2NUkpkPy1bOP/VlFLpW9KWaX0Y2f9WemEn8SZ84O2mz0A5LeXD2pMfr7Sv1fKfvpdW+5RunRXstI/lzdV2kgQt1HYXnHgNI+TXAG5UZGmc+uOJm+cjHFXUazS8dp2+M5CqX/IqzlGyqXtoRK556lOsfI4Ssy2Eo9bS1WguZX09AAw5HzhTu3G6ekBNLeR4dAcTNpuCV6neTHSZtHcRuaMCdKX9lvk0cZhsWnpATSrjENLA6D/8XHZxPUEwJRtVk8P4PWTNkBfQrbpDjTNtWS16O6kKtIDaOewaX8fTW30e+PXheSn3Gfp8TZVHKxvNKSxNnIsDfC98NQyq3Rt2jZ0QvfKCSGEENIgCerkZfr06bBYLKZXp06d1PHS0lJMmDABCQkJiIyMxPDhw5GXl1dFj4QQQkjD4Phjo9q8QpWgr7ycddZZ2Lt3r3p9+eWX6tjkyZPx/vvv480338SqVauwZ88eXHXVVUEcLSGEEFI3eGGt9StUCXrMi91uR3Jy8knlBQUFmD9/PhYtWoSBAwcCABYuXIjOnTtj9erVOOecc870UAkhhBBSDwj6tG3z5s1ITU1FmzZtMHLkSOzcuRMAkJOTg4qKCmRmZqq6nTp1QsuWLZGdne23v7KyMhQWFppehBBCSH3DY1hq/QpVgrrykp6ejldeeQUdO3bE3r17MWPGDPzhD3/ATz/9hNzcXDidTsTGxpraJCUlITc312+fs2bNwowZM04qT56/HnaLE+cOGqXKEr/4TukJH4xRutMbu5V+YEQfUz85h1oo/X8dFiv9RnRXpT/pcrbSxgbJYfT0r5IzaXWffyl9V29J0tP8bTn3m/n9TOc2OoqryKK5aNaVycrVwCablP40V/r96kh7pcuTxEnk2n5I6bMixam0N76v6dxFXnH5xMbLOHILJKdTlFUcQ84j4oLZ74mQa6gUt1FRkYzP4pQ8TCe5jcI0m4/mVvK4fdsLIjS3kcUuH/Fwq+ZCcmi5jXTHjuPE3EYyv/eX28iw6fmCRNtMuY0Em1XLU6S5dOwWcy6rCs3GpLuKyg3f12RyFcGfq0jPeeT/u4u/Z+n+3EPV6Ud3JwWEHzdJTceEquo3QMcKabjQKh04QZ28XHzxxUp3794d6enpaNWqFf7zn/8g7ITEgNVlypQpyMrKUj8XFhaiRYsWVbQghBBCzjxGLbNKG9xht34QGxuLDh06YMuWLUhOTkZ5eTnytf1MACAvL89njMxxXC4XoqOjTS9CCCGENB7q1eSlqKgIW7duRUpKCvr06QOHw4Hly5er45s2bcLOnTuRkZERxFESQgghtccDS61foUpQHxvddddduOyyy9CqVSvs2bMH06ZNg81mw3XXXYeYmBiMHTsWWVlZiI+PR3R0NG677TZkZGTQaUQIIaTB4zVqF7fiDeEYraBOXn7//Xdcd911OHjwIBITEzFgwACsXr0aiYmJAICnnnoKVqsVw4cPR1lZGQYPHoznnnsumEMmhBBCSJAJ6uRl8eLFVR53u92YO3cu5s6dW+tzWVs1h9XmQpPHtEDgDMlh1PGlw0pX7til9HtL+pv6cWjO67i7JLnO2JhflH5loAQiJ/0ibqOK7HgZTx+ZbRf3lnxJnjni+Plg61mmc/dvtU3pfU0Tlf6sQN7GMU1kk79P9jdXeu2BVkofTRFnjzNHrruLW5xOHyf80XTuQ17Ju9M6VhxK322WfE1hFrkfjkJxweRWSi4lw6M5e4o1+45ba1use3OA8kjt6abmNvL6cxs55NweLYdRlFXus9cp5S49388JjiKrtixr+PltMey620jGbnYVaXmfbHIPdIeQw2K+bt0Gacpt5Nc9pDt7Tu0q0utbLf6P2aqxNF1Tx09V9avTl81SjSfedfittCHlzQlorA3o+hoT3loG7NambUMndK+cEEIICSJeWGr9CoS5c+ciLS0Nbrcb6enpWLt2rd+6r7zyyklpfNxut6mOYRiYOnUqUlJSEBYWhszMTGzevDmgsVUXTl4IIYSQEOGNN95AVlYWpk2bhnXr1qFHjx4YPHgw9u3b57dNdHS0KY3Pjh07TMcfe+wxzJkzB/PmzcOaNWsQERGBwYMHo7S09LRdBycvhBBCSBAIxg67Tz75JMaNG4cxY8agS5cumDdvHsLDw7FgwQK/bSwWC5KTk9UrKSlJHTMMA7Nnz8b999+PK664At27d8drr72GPXv2YMmSJYHclmrByQshhBASBI7HvNTmVRPKy8uRk5NjSrtjtVqRmZlZZdqdoqIitGrVCi1atMAVV1yBDRs2qGPbtm1Dbm6uqc+YmBikp6dX2Wdt4eSFEEIIacCcmM+vrKzMZ70DBw7A4/GYVk6AqtPudOzYEQsWLMC7776Lf/3rX/B6vejfvz9+//13AFDtatJnXRD0rNJnik2TomENc6P9X75VZZtfkfw97cf8qLQxQFxIrd/Yb+7IkLD8J/8ibqBJ8dK+cmCB0rZPWyud8rU8/8sZL11e0eUHpX+qFFeJsSHKdOpLe6xX+sW0K5X+em+s0o8kf6G0p0hyEG3f00HpyGRZaozR6rS1i4uoNFHcPwCwu1LyE7WNPKD094fbwhfWIrnWXRXxPuvYirScRZrbyF5izvFTkij1jHJxEnndZnfOcaKdcu58LbdRhFV+oQ2HzNudWs4d7wm5jXRXi7/cRtDcRh7t82G3yfgqNDeH06o5rvTcRlb/uY2sFu8pyz3adxGbntsIFp/llYbv+oD/vSdMDiWtX3/lRh26WPy6kBqSU6YhjZWcdryoZW6j//+7dmIKnGnTpmH69Om1GZoiIyPDtDFs//790blzZ7zwwgt48MEH6+QcgRAykxdCCCGkPmHUwjF0vD0A7Nq1y5QKx+Vy+azfpEkT2Gw25OXlmcpPlXZHx+FwoFevXtiyZQsAqHZ5eXlISUkx9dmzZ89qX0tN4WMjQgghJAgczypdmxeAk/L5+Zu8OJ1O9OnTx5R2x+v1Yvny5dVOu+PxePDjjz+qiUrr1q2RnJxs6rOwsBBr1qw5ral8uPJCCCGEhAhZWVkYPXo0+vbti379+mH27NkoLi7GmDFjAACjRo1Cs2bNMGvWLADAzJkzcc4556Bdu3bIz8/H448/jh07duCmm24CcMyJNGnSJPzjH/9A+/bt0bp1azzwwANITU3FsGHDTtt1cPJCCCGEBIFg7LB77bXXYv/+/Zg6dSpyc3PRs2dPLF26VAXc7ty5E1ar9Hv48GGMGzcOubm5iIuLQ58+ffD111+jS5cuqs4999yD4uJijB8/Hvn5+RgwYACWLl160mZ2dUnITF4+PP85REVZMeL6u1XZ2+fPVnrS0NuU3nmJPINsf6v/XQJf/vwCpSMzJUh06lkfKv3EOdcpnbDkZ6UX7D9P6duaynLbfYmXSv2fzAGp57r3Kv1Yh3ClC7ZJyoPIPtqHxZD2zl2yjFiSIlGDRqVst59kk8DYkkTzL8VvFU2V7hQmKQycBVrQprZVvaVItuLfXRan9SRBqY4jWmBnmKQssBVXQqcyTMZuVMoxi0sLcNUCa6PsEpib75B7owfsep36tvwyDs8JAbtW7cmq199viyk9gGiH3Xdgrh6wqwffuqzm69YDcPX0ACVaSgGnVl6plVu196LCq5XrqRCqCBT0d8xf0Ky/wNyAgmxrEcBYHeoyiNj/Sc5QG9Kg0R/9BNo+ECZOnIiJEyf6PLZy5UrTz0899RSeeuqpKvuzWCyYOXMmZs6cGdB4AoExL4QQQghpUITMygshhBBSn6hNfqLj7UMVTl4IIYSQIBCsx0aNAT42IoQQQkiDgisvhBBCSBDgykvghMzk5bDXgQqvFd0nfa/K2mlXX/jXQqUf7LBM6f/re4mpH9uhI0qnvS/ukNlxg5TePGi+0vdmiBsk9lVJG/DZBklNMK/5l0pXtmumdPTGw6ZzJ9silS5oLx/ayO2iywxxD1nDNKfNbumnYIA4oyyawyjSKq6eo2IuAgBsKZW8FRdEiWvKlS91Srzl2g/iNvq9RNxGFptck10yE8AbrrmNSrR+AFS6JTWBUSH33OHWnEfadUQ7tDTsDtnTP9yipRZwak4eVCMFAADNGGRyVsGmb9GvpQewSrlXc5LY/aQH0B1FAFBhchXJtRYY8r7qriI9w6zNop/bd3oA85b+Zgw/x2rqQvJHTesfaxR4fWt1F5lrOq4z8Z9HCP8H1djh5CVw+NiIEEIIIQ2KkFl5IYQQQuoTXHkJHE5eCCGEkCBgoHZ251De15CTF0IIISQIcOUlcBjzQgghhJAGRcisvPz5w1tgDXNj69UvqLILN/5J6Y96ikNId/VMu140AET9FiX1XlqndELzXkofOL9E6SF9flB6R7NUpWNyxF1TcqG4YA6dJc6axMVbTefWnUToUCR9/VvcJ9sqpS9rvLh8onaJWyWm6SGpEy45knRHRlkTc16lX4vEbXRD7BqlXfmycFlkyDmMo+L42VuSqHSEUyxGDrkEeMLE5mM7LPfv2DHRhkccOW6X3A+T28imdeyQj7hbyx2ku42s2rKtV96Wk/A65Fo9Wt4oq11z9ui5jWwy1nLt3ppzG+k5lsxuI4+hj1F3CUm57iqq1HIYmVxFfpalq8xt5KeNnhfIVo3lbrNryfd3JesJ/dQ891DofvskDRuuvAROyExeCCGEkPoEJy+Bw8dGhBBCCGlQcOWFEEIICQJceQkcTl4IIYSQIGAYlsB2m9bahyp8bEQIIYSQBkXIrLx0eHY37FYXJg/oo8pKXpI8QqWPicXhzaIYpbOGfGDq5/Wd/eSHBTL3S/x8j9JPHjhX6dubrlB6fL/JSjfNEdfNZ0cTlD7cVdwj8UWaawbAr1pen3Nb/ab0nu0tlf76aBulvcnxSoftlr46xUuio1/jkpWu0NxCtqaSmwgAthVIX01ayXW78sUhs98jbhejXFxPB4+IgyrSKXYeZ5Hc88pwLY9PWZnp3JWa2wiayyfCpeVAssmYomzidDJcmttIyw/kceo5heTc3ip+IwyHbxuM7jbyaFYZp01zN2nfkFya66lcS5jk0MoBsxMp3Frms9ym5zbSXDdWi3Zvq+FCsp3wBc6UD8miubH8upB81/dnHKq5oyjINLTxNmIsjei98MJSq03qatO2oRMykxdCCCGkPsGYl8DhYyNCCCGENCi48kIIIYQEAQbsBg4nL4QQQkgQ4GOjwAmZyYtRWATDUo7sp89WZbFvfav0xX+6RemygxIh+ttlL5n6SQ+XQNm7LpigtPPDtUr/Jztd6UeGfa/03v7ylK79QzuUfmn3eUqndslT2hZpTk2wrLiL0pfFr5f2e2SL/88Pd1K6pLkWKLtWztc7UvQviVL/sFcCXZsn5JvOvXOfBOxGWrSg23zZon+3RwKdvVpw8dEjbqUtbpfSjiIJNq2IkKBSlJoDdj1hviP0olwyXotD0gtE2STY2HBIv25tK309YNeUHkC6OQkt7tWUBsCmBexWQA/Y1dMDSGO7lh7Aqz25PTE9gCkw16KnJvDdxhRkqwXy6uVW7W9dVX/4/H2j81fur6+a9nPsYM3G5D8quIb1CTnDcOUlcBjzQgghhJAGRcisvBBCCCH1CaOWj41CeeWFkxdCCCEkCBio3Z5HofwElI+NCCGEENKg4MoLIYQQEgS8sMDCHXYDImQmLzv/2hk2lxvNH1qtymytZVv91BfEBWMtF6fMskzzLbpQ26p+x+WiO3/bVOlmn0n59kuPKN01XZxKJfn5Sm/5Xhw/ky78WOmPW2eYzv3hXnH8vN7x30rPO3hY6W9+76x0WAtte/mlcr5urt+VPpoiTqU9HrnWTrHiegKAHRtTlHZYtC3t88Xxs708URpo2/hbCrR7GCY30FEkTpniFLH5GGXatv8AvGFe+CLWKecucmpuI6uUe11S7tYdO5rbyGaRBciq3EZwaA4ezc1jt+npAaS6U3MVVWgOIXN6AHmPTnQbeUxOJGljTg+gpQHwU+7vD5zZnWTxe8zkxvLrHvJZXGv09+ZMUGdbz5+B9fwajzWUnzHUU+g2Chw+NiKEEEJIgyJkVl4IIYSQ+oTXsMDCTeoCgpMXQgghJAgYRi3dRiH8KJCPjQghhBDSoODKCyGEEBIEGLAbOCEzefn7dW8gPMqGFz+7UpVtuUxy/6T9/WuprDkc/vrlKFM/j/d/U+nJf/hE6f8MuFjp6P9tVfrFQ+cqndX8U6VnxUg+o8Qc+QBeduUGpRefJX0CwN6t0Uond5G8R0al5BfybJNrKm4ubb1l4sBpYddcPsnyEdhcnqR014jdpnN/frg3fGEpLFF6e2kTn3UcR+R+GhF6biNx0FSGS74ko9zsNkKY1NPfmxin5DAqcsi90XMbeZ1S3+3HVWRFdd1Gen4h0Q7tflZozhw9t5G/fEQVht1nOQCUeOWe6LmKdFeRFbrTSc9hpLmNTK4izZ3k9b/w6j8nkWjdhVRneYcC6ctvPzWrHxB0/JBawMlL4ITM5IUQQgipTzBgN3DqTczLI488AovFgkmTJqmy0tJSTJgwAQkJCYiMjMTw4cORl5fnvxNCCCGEVMncuXORlpYGt9uN9PR0rF271m/dl156CX/4wx8QFxeHuLg4ZGZmnlT/xhtvhMViMb2GDBlyWq+hXkxevvnmG7zwwgvo3r27qXzy5Ml4//338eabb2LVqlXYs2cPrrrqqiCNkhBCCKk7jruNavOqKW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/VXrlyJ6667Dp9//jmys7PRokULXHTRRdi92xxaMGTIEOzdu1e9/v3vf/vsr64I+uSlqKgII0eOxEsvvYS4uDhVXlBQgPnz5+PJJ5/EwIED0adPHyxcuBBff/01Vq9eXUWPhBBCSP3n2ATEUotXzc/55JNPYty4cRgzZgy6dOmCefPmITw8HAsWLPBZ//XXX8ett96Knj17olOnTnj55Zfh9XqxfPlyUz2Xy4Xk5GT10v8/Px0EffIyYcIEDB06FJmZmabynJwcVFRUmMo7deqEli1bIjs7229/ZWVlKCwsNL0IIYSQxsqJ/+eVlZX5rFdeXo6cnBzT/6tWqxWZmZlV/r+qU1JSgoqKCsTHx5vKV65ciaZNm6Jjx4645ZZbcPDgwcAvqBoENWB38eLFWLduHb755puTjuXm5sLpdCI2NtZUnpSUhNzcXL99zpo1CzNmzDip/KLwg4gOt2L6veJcmdL5v0q/8V/tzTwoE560Reb53X2O4Ur//MeXlX5qkEyBw/+7X+nF6/sq/Y+L1ivt7dRK6fjvJDdRmj1K6YNdzOeO/FV+LjPEYWTV8gVFbZcArsL+4rrRXTpxVqlfLCmL8NNRsSedF/mL6dxu7XN41NB+MYqLldxRkiCns+Ur7dDmj94wLYdUsbiKKsIlx5JRobmLADg0t5HFJk6bWIdc326nOJ2iLHpuIy13kDZX13Mb6ZzoNtJzGEHLYVShleuuIq8pt5GWjwgyDj23kZ6nyGkxX3eBoeWBsujOJT0vk+ZC8vrJeaS5ivRPlO5UOPFbjP8cRjULEAzIDVELB4+1Ot/HAhrTGQiMDOHgy1ClrtxGLVq0MJVPmzYN06dPP6n+gQMH4PF4kJSUZCpPSkrCL7/8clJ9X9x7771ITU01TYCGDBmCq666Cq1bt8bWrVvxt7/9DRdffDGys7Nh0/5m1yVBm7zs2rULd9xxB5YtWwa3211n/U6ZMgVZWVnq58LCwpPeWEIIISTYGKide/542127diE6WraLcLlcvhvUkkceeQSLFy/GypUrTf9vjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrEE7bFRTk4O9u3bh969e8Nut8Nut2PVqlWYM2cO7HY7kpKSUF5ejnwt+zIA5OXlITk52W+/LpcL0dHRphchhBDSWDnx/zx/k5cmTZrAZrOd5No91f+rAPDEE0/gkUcewaeffnqSueZE2rRpgyZNmmDLli01u5AaELTJy6BBg/Djjz9i/fr16tW3b1+MHDlSaYfDYQoK2rRpE3bu3ImMjIxgDZsQQgipE2oXrFvzR05OpxN9+vQx/b96PPi2qv9XH3vsMTz44INYunQp+vbt67fecX7//XccPHgQKSkpp6wbKEF7bBQVFYWuXbuayiIiIpCQkKDKx44di6ysLMTHxyM6Ohq33XYbMjIycM455wRjyIQQQkjdUVfPjWpAVlYWRo8ejb59+6Jfv36YPXs2iouLMWbMGADAqFGj0KxZM8yaNQsA8Oijj2Lq1KlYtGgR0tLSVMxpZGQkIiMjUVRUhBkzZmD48OFITk7G1q1bcc8996Bdu3YYPHhwLS6uaur1DrtPPfUUrFYrhg8fjrKyMgwePBjPPfdcQH1duP462MJdyD5b7GCRVnlmN/Ovsq1+5OYYpVNnm4OJE+P7KL17gASMXpOxRukf07Rg3K9l+a4gUwJJ9/WR7f2TFn6ntB4MazvL7JSKe1WCWn/VglqtiRIoG/ObBPIm/kkCh23REgisBzWWJksg6M9HZJY8Kk6uBwDch+S35JBXAm29xZIeYKfWPtop5c4j0o8nUiJinb9LsG+lXBoMj3mb/DC3nE8P2I22FUklh3yUI6xS3+PSt+XX0gDIzvsmvA7zXwOPIQGxVodor/ZXw2mX96Jcu7dOq54GQAsctvhOG2A94S+RVztWncBcL/wEIfv5duavPmDeP8JWjTQA5uBf3wu65nQCfk9dBWciaPb0n4JUD0sovBe1DNgNJMj72muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW+R1+/vnnUV5ejj/96U+mfo4HBdtsNvzwww949dVXkZ+fj9TUVFx00UV48MEHT1vsDVDPJi8rV640/ex2uzF37lzMnTs3OAMihBBCGhkTJ07ExIkTfR478f/h7du3V9lXWFgYPvnkkyrrnA7q1eSFEEIICRUC3SVXbx+qcPJCCCGEBAFmlQ6coO+wSwghhBBSE7jyQgghhAQDw1K7nZVDeOUlZCYvTWa7YLe78fkrko/hqyPtlX71wpeUXtDjD0rve12cPAAQ9+lmpe+8VTJcP5/2jtKXXiB++abZh5T+d2EHpQ/31lxBz4lr6dsyic6+rM1PpnP/sFnaf1IkNvPK5rI1ftiOAqXPTtgu/SZKW93RFJYsjp8th+Rak9LMHw33QXHU7KkUl5ZRLtdxsFAcWzFhUsdZKA9mK6KkX+dRcV9VSlNAc/gAQJRbxmvRXEXxdnEbGW5xMbl1N4/TtwvGc0IagON4nSc4fjT7ic2upQfQHjY7TK4iOYeeBqDcsGv1facHCLea85GYHErQrklz3VgtehqAU7uQbNrfOt2FZLOY/whW5UTy1cbfo3e/z+SreFZf46Vwf/VDOB6ANAwY8xI4fGxECCGEkAZFyKy8EEIIIfWKIGxS11jg5IUQQggJAnQbBQ4fGxFCCCGkQcGVF0IIISRYhPCjn9oQMpMXy+oNsFgcuOdfo1WZU0sdNOPOb5Xu1XyZ0n/402RTP0nPSc6fTR+lK93kNknOc2iQuGgSXv9N6Wd/Pl/p87tuUnpfiqQif+uQJN0Z1eQr07l/+F3yE3209yylj7YJUzrug+1KZ0SKM+rr1LOVzvNI7p8OiZL/6PstLZTW8z4BgOuQOGG2V4i7yagUt1FZvrSxhMmYXIXilCmPlMW+iFLp0xNudhjpxLrEjeVxik0oxib5k7wuKQ/X8gB5XLrbSLS/3EaG/US3kfRlt+uOH6nntvt2D7n8uIrcFt/lTos5p1OF7h7ym9tIy7ek5xfSVpMrvVqOpGrkKarqmL88SdXJeeQPm6WaC8B19Ee+oeXMqfF4G9j1hTJ8bBQ4ITN5IYQQQuoVDNgNGMa8EEIIIaRBwZUXQgghJChY/v+rNu1DE05eCCGEkGDAx0YBw8dGhBBCCGlQBLTyUlxcjEceeQTLly/Hvn374PWanSK//fabn5bB48jVfWFzutF6zkZVZtFcDtdfeYnS/2z1ttJpV2819XN0rbh8Wn4geYv+OyZO6azenyn9nlecRPZscQvdNGGV0lO7jlN66dYkpZ9IyTad23PkiNLbt3ZWOixNlg6jC8RC1cVxUOni5uIE2lwhY+0Tu1PpDfvawh+2fHH2bClL8lnHni8uGISL28hRKO6aomQtL1KZuI2MCLPTRifeJec+4JDcT9E2cXV53bqbR+6H7jbSXS1+3UZO82fZo+cwsus5jKSO26bnMNLcRjbdVaTlNrKcuvzYMd+5ivT8SXp5pZ9yfw4hj+ZCsp6w/GxyLpkcSj67qvE3wCpdEvXx22RNr7s+XgOpf3DlJWACmrzcdNNNWLVqFf785z8jJSUFFkvoPncjhBBCAoJZpQMmoMnLxx9/jA8//BDnnntuXY+HEEIIIaRKApq8xMXFIT4+vq7HQgghhIQMhlHFo9hqtg9VAgrYffDBBzF16lSUlJScujIhhBBCTsaog1eIEtDKyz//+U9s3boVSUlJSEtLg8PhMB1ft25dnQyOEEIIIeREApq8DBs2rI6HcfrpesuPcEY6sWtlrCozioqV/v3lDkr/+fpRSi8561+mfs697C6lW93/tdIzNwxVem2/hUp/0DFD6ZSv5Xz9ssTVsq+3WF9sP2n6D+aFMYtdJolRv8pbd+QsyS8EQ/pNsYnj50hzCez6/mgrpXuEi9sobJ90U2ZofQJAYZGSvxSJg8pik3JXvpY7KEpcRfYj4iqqiNDcRuWSY8kWLuez2DTXEoB4p9y3Ay5xbEVZJeeR1yVtHJqryJzbSCvX3EZ6/iKL48TcRvKzy5TDSPp1WjUXEjS3kUWuSXchObQcRiVel8/yY+fQ28i59dxGVi3xjdkhJJhdRfBZ/0T8LUfXOJdKXQYUamPS30v/DqgAzh3CAZAkCDBgN2ACmrxMmzatrsdBCCGEhBQWo3aJQhtaktG6pFY77Obk5GDjxmP7ppx11lno1atXnQyKEEIIafRwn5eACWjysm/fPowYMQIrV65EbGwsACA/Px8XXHABFi9ejMTExLocIyGEEEKIIiC30W233YYjR45gw4YNOHToEA4dOoSffvoJhYWFuP322+t6jIQQQkjj43jMS21eIUpAKy9Lly7FZ599hs6dZYv6Ll26YO7cubjooovqbHB1yexmaxEdZUO7O29WZdFb5Y1PelkcUget8vjLM9O8LjdwsNTb8Xyq0vYVMdLmbGmzP6OJ0omLf1BaDwSt6CNBr03/LUG2v1ZIQCoA2JJkRStuswRwJlwskba2KAlodVjk7S1pLkGpOQUSsHtF1PdKhx2QMR32SpAtABhHZIzbjsg4IpwSdOvMl/qVURKI6txTIOWRcp+8FXIN4WHSjx6YDAAJDmkPl/QbZZUxerT0AA49MFeqm/A65Vo9WpCz1X5C0KwWzOvUjpVr53DbJDBXD7J1Wf2lAfD4rG+zmFMT6IG5pu3+te36TekBvL6/i3j9ZJ7VA11tJ9TxF5hrmIKCrX7Ka5tOoBH/QQ7h/2yID/jYKGACWnnxer0n2aMBwOFwnJTniBBCCCGkLglo8jJw4EDccccd2LNnjyrbvXs3Jk+ejEGDBtXZ4AghhJBGCzepC5iAJi/PPvssCgsLkZaWhrZt26Jt27Zo3bo1CgsL8cwzz9T1GAkhhJDGBycvARNQzEuLFi2wbt06fPbZZ/jll18AAJ07d0ZmZmadDo4QQggh5EQC3ufFYrHgwgsvxIUXXliX4yGEEEJCA+6wGzDVnrzMmTMH48ePh9vtxpw5c6qsWx/t0vfu7QPnEQf+faU81lp4YIDSO5ZIluwm72xUeszoq039vNbuLaWHXCypAlJWHFB6/s2dlD7YX1w08S+LYye7TPanv7rDd0qv2yht3z3Sw3TuijZJSkdsPqx0etJmpdckiwPsqCFunLDmR5TeeKCpjLuVfATC9ok7Zlel2abjPSrOp7zD0Uq3DctX2p2vOaiipV/nFmlbEal1qrl8YsKljsVh/ljG2+W+GW4JFI/Qtt+v1NIAOCzi0vGcHFcOwOw20p1fdueJW/TLMbdddxXJE1fdVVSuu4pMbiMZU7jmkjKlAMCJ59a29de20vTnQqrU6tu0v2l6GgCbRUvhUIWrx/DTxq95qIauohqnGQD8/6Gu6dJ5CC+110dCeZdY7rAbONWevDz11FMYOXIk3G43nnrqKb/1LBZLvZy8EEIIIaRxUO2A3W3btiEhIUFpf6/ffvvttA2WEEIIaTQEKWB37ty5SEtLg9vtRnp6OtauXVtl/TfffBOdOnWC2+1Gt27d8NFHH5kvwzAwdepUpKSkICwsDJmZmdi8ebOf3uqGgNxGM2fORElJyUnlR48excyZM2s9KEIIIYTUPW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/W//vprXHfddRg7diy+++47DBs2DMOGDcNPP/2k6jz22GOYM2cO5s2bhzVr1iAiIgKDBw9GaWnpabuOgCYvM2bMQFFR0UnlJSUlmDFjRq0HRQghhDR2LJC4l4BeAZzzySefxLhx4zBmzBh06dIF8+bNQ3h4OBYsWOCz/tNPP40hQ4bg7rvvRufOnfHggw+id+/eePbZZwEcW3WZPXs27r//flxxxRXo3r07XnvtNezZswdLliwJ+N6cioAmL4ZhwGI5+bZ9//33iI+P99GCEEIIIaeDwsJC06usrMxnvfLycuTk5Ji2NbFarcjMzER2drbPNtnZ2SdtgzJ48GBVf9u2bcjNzTXViYmJQXp6ut8+64IaWaXj4uJgsVhgsVjQoUMH0wTG4/GgqKgIN998cxU9BI9v5/WEzenGlIc+V2XPNpMb23XsBKVbPS7un9z/dDH14/qbOD0qLhPHj2f+FqWf/nag0n/qJbmQNqRJTqH5ebFK/z1Vnh9+u0PsOG/tkBxLAGBp71a6yXo53wWR4o5a1aq/0r9XijumR7Lshpz9Y3ulI63Sp3u/OH5+KU8xndvwaPl4DkobS3i40q4CqVMaq7lrNKdSZaTZUXOcBLc8hix3mZ1O8fZipb1hYh8K13IBVYbJPNzkNpKhmjAcuttI+rGfkNuoXHcb2Xy7h8Js5T7L3RapX2rIuGMtcq0VXv+5jbyae8ipOZEqTS4kvb6e80j73fST86gqx4/He+rcRtXtS43JUs3vSnXkoGhoTowaj7eBXR/xQR1ZpVu0aGEqnjZtGqZPn35S9QMHDsDj8SApKclUnpSUpPZsO5Hc3Fyf9XNzc9Xx42X+6pwOajR5mT17NgzDwF/+8hfMmDEDMTGSZM/pdCItLQ0ZGRl1PkhCCCGk0VFHiRl37dqF6GjZwsLl8pORthFRo8nL6NGjAQCtW7dG//79fSZnJIQQQsiZIzo62jR58UeTJk1gs9mQl5dnKs/Ly0NycrLPNsnJyVXWP/5vXl4eUlJSTHV69uxZk8uoEdWOeSksLFS6V69eOHr06EnP2Y6/CCGEEHIKzrBV2ul0ok+fPli+fLkq83q9WL58ud+nJhkZGab6ALBs2TJVv3Xr1khOTjbVKSwsxJo1a07rk5hqr7zExcVh7969aNq0KWJjY30G7B4P5PV4fMc1EEIIIeQYwdhhNysrC6NHj0bfvn3Rr18/zJ49G8XFxRgzZgwAYNSoUWjWrBlmzZoFALjjjjvwxz/+Ef/85z8xdOhQLF68GN9++y1efPHFY2OwWDBp0iT84x//QPv27dG6dWs88MADSE1NxbBhwwK/uFNQ7cnLihUrlJPo888/P0VtQgghhNQ3rr32Wuzfvx9Tp05Fbm4uevbsiaVLl6qA2507d8JqlYcy/fv3x6JFi3D//ffjb3/7G9q3b48lS5aga9euqs4999yD4uJijB8/Hvn5+RgwYACWLl0Kt9uPY6IOqPbk5Y9//KNP3VCIXvwt7BYH+g+S1AWP939T6b9e+7HSb/10kdIp/xVXDwA8PL6f0s90W6z0rOjzlE5cIXmLbr3gC6VHpksupI0/SGR2p1arlNZzCOVvSDCd29JBVrvijkiuoi5OceMUpsm515c1U/rcWLmOdbmS/0jHelj27tlQ0sxnHQBwHhKHjBETIeWHxXVT2FJcSMZRbaOiSHHgQHOfJLrl3Ltd5me3sTa5Po9bPrJu3e2mxadZtaehXrkdZlzi7PFojiKn44T8Qpprx23XcxjJPdBzG5UaTq1c3F4l2kAcmgupTHMhVTe3kdlVpOc8OrWryGpyIfkuP7GN+YDvYn+OiRr3UwV+8yfVJTXM0UTHD6kVdRSwW1MmTpyIiRMn+jy2cuXKk8quvvpqXH311SdX/v9YLBbMnDnzjG5SG9A+L0uXLsWXX36pfp47dy569uyJ66+/HocPH66ipZnnn38e3bt3V8FGGRkZ+PhjmUSUlpZiwoQJSEhIQGRkJIYPH35S4BAhhBDSIAlSeoDGQECTl7vvvlsF5v7444/IysrCJZdcgm3btiErK6va/TRv3hyPPPIIcnJy8O2332LgwIG44oorsGHDBgDA5MmT8f777+PNN9/EqlWrsGfPHlx11VWBDJkQQgghjYQaWaWPs23bNnTpcmzztv/+97+47LLL8PDDD2PdunW45JJLqt3PZZddZvr5oYcewvPPP4/Vq1ejefPmmD9/PhYtWoSBA49t+rZw4UJ07twZq1evxjnnnBPI0AkhhJB6QTACdhsLAa28OJ1OlZjxs88+w0UXHYsRiY+PD9gq7fF4sHjxYhQXFyMjIwM5OTmoqKgwbTncqVMntGzZssoth8vKymjdJoQQUv85vsNubV4hSkArLwMGDEBWVhbOPfdcrF27Fm+88QYA4Ndff0Xz5s1r1NePP/6IjIwMlJaWIjIyEu+88w66dOmC9evXw+l0IjY21lT/VFsOz5o1y3dyyLO7AHY3Oj0ugaH3jhmp9Jbr5ik973rZvj3iPXMMz3+Wy/b7D1/3g9LTzumgdJOVvyudZo9SOleaIn6d3PrDQ+V8Nm3X4vgN5ksoulwmYlanBIAmWCVo9kia1P/6SDulr49frXT4XqlT6JUAYSO/QOmNheYNiyz2g0q7RMITHSZjL5S+yqO0gN1yCeR1RYq22CTotYlTC9h1J5rOHWuV++MJ0wN2pX2l2/cvsR7Iq6cBsGiBuRVauR6UCwAV2h8Ht00CcPXt/vWAXT09gFMLzC0w5D45LNq5tSDbE9MDVOqpA/wE5urfPsyBuYI3gC39a5oGwG8wbSDfDLU21up8v6rpH/Az8Qc/hP9TITUgSAG7jYGAVl6effZZ2O12vPXWW3j++efRrNkxZ8rHH3+MIUOG1Kivjh07Yv369VizZg1uueUWjB49Gj///HMgwwIATJkyBQUFBeq1a9eugPsihBBCSP0joJWXli1b4oMPPjip/KmnnqpxX06nE+3aHVsh6NOnD7755hs8/fTTuPbaa1FeXo78/HzT6ktV2xgDx3I6hEJeB0IIIQ0bxrwETkCTF+BYjMqSJUuwceOxjMZnnXUWLr/8cti0RwGB4PV6UVZWhj59+sDhcGD58uUYPnw4AGDTpk3YuXMnkz8SQghp+PCxUcAENHnZsmULLrnkEuzevRsdO3YEcCzWpEWLFvjwww/Rtm3bavUzZcoUXHzxxWjZsiWOHDmCRYsWYeXKlfjkk08QExODsWPHIisrC/Hx8YiOjsZtt92GjIwMOo0IIYSQECagycvtt9+Otm3bYvXq1SplwMGDB3HDDTfg9ttvx4cfflitfvbt24dRo0Zh7969iImJQffu3fHJJ5/gwgsvBHDsMZTVasXw4cNRVlaGwYMH47nnngtkyIQQQkj9opaPjbjyUkNWrVplmrgAQEJCAh555BGce+651e5n/vz5VR53u92YO3cu5s6dG8gwTeyd5IEtvBLNr92myjq+KK6PWReJW+hffRcofdfgCaZ+Wr9XpvSyYXL7dmWKbvOJBAlvrhAXzTlnb1L64P+JK+ujYtFGG9mWP/4nSQEAAGfftlnpbU3FkVNhiKulsrU4ftbub6X0/UmSpiByr1z3Lo98+o0i2YZ/68FU07lbRki/7sPSpjxGXE/hu8WGVBEtqQ0MLVFnTLikCrA4xbGT5Dgg9cPMe/pHWcWhVBkmMeYui9xzj58wJ69LxuoxxM1jc2qOH0N3G4mj6NgxOV+Y5jaqgDweDbfJZ6LUK9cUbpXyMq1cdyHp7iTHiW4j7dwmt5FebvFTX0+dYEonIFp3DtlOSLSq/03UHT+GyQlUG0fNmXD8nP5TkOoRyrEZVcLHRgETkNvI5XLhyJEjJ5UXFRXB6fSXTIYQQgghpPYENHm59NJLMX78eKxZswaGYcAwDKxevRo333wzLr/88roeIyGEENL4YG6jgAlo8jJnzhy0a9cO/fv3h9vthtvtxrnnnot27drh6aefrusxEkIIIY2O41bp2rxClRrFvHi9Xjz++ON47733UF5ejmHDhmH06NGwWCzo3Lmz2q+FEEIIIeR0UaPJy0MPPYTp06cjMzMTYWFh+OijjxATE4MFCxacujEhhBBCSB1Qo8nLa6+9hueeew5//etfARxLyjh06FC8/PLLsFoDegJ1xvis9/8hOsqKP4yfrMqSnl+j9OuLBil9722/KL3rOrP7pP1fJOHQpPXXKj3gD1K+L0V2AH52/wVSP2WZ0tM2Sfn8nQOUPnqW5DaK+2Cj6dyXxq1X+p9trld6t0ecQN1a7FH6+y0tlE7oLvmPwnKl/s9lKUp7tRxExQekPgBYIrX2B8WpUxanuW6Kpd+KaLNz5jiJ4eK+8rjFItTELnmbvGFm61CUlguoMkzP36M5atw+TwevU8ZRCenHYcptJGuvYQ7z+11qyK9ImFXLbaS5h9x+3ENOUw4j3VWkXY8pf5H/3EZW3VWk50Py4x7S8VfuL+dRIH3pz95tFt2ddOr6Jx+rWZuGtHQe0Fgb0PWRGkK3UcDUaMaxc+dOXHLJJernzMxMWCwW7Nmzp4pWhBBCCDkRxrwETo0mL5WVlXC7zV9xHQ4HKioq/LQghBBCCKlbavTYyDAM3HjjjabEh6Wlpbj55psRESGPFd5+++26GyEhhBDSWAnh1ZPaUKPJy+jRo08qu+GGG+psMIQQQkjIwJiXgKnR5GXhwoWnaxyEEEIIIdUioNxGDZH/lcYh3GHDyJs/VWXv/S4Oo1av/Kb0jBHdlH65/6umfh5zSe6myPejlL5/5sdKjx5wp5zjO3H8PD30G6U9WnqFXeu7Km05S5wW0f/KN537bNdhpfPbhymdUyZ5iAY2EafUL5/7zu5t2y/Onu9KWvms49pn/mgY0ZFy7KC4ko40D1faW1Ki1RcHDjT3SdMwue5ct9y/BLvmQgo3n9ut5d3R3UYOPbeRH7eR4RIHj0dLzONyyvhKNXdL+Am5jco191CYrVxrI2kwXJoLqcQr5Q5/OYyg51WyavWrmdvI6ztUzaOVm5xYXt8OLa+3ivxC/r7R1cKFVG+o0ukUQBtCAqS2QbehHLAbMpMXQgghpF7Bx0YBU783ZyGEEEIIOQGuvBBCCCFBgI+NAoeTF0IIISQY8LFRwPCxESGEEEIaFCGz8vLAG9fD5nZjw/i5quyt8b2U9n4qDpw33j1P6Rk3Sc4iAJg6qIvSTT7ZpnTbh8WNsydTXCOJX8st3jdEHDW2+Dips06mz0evE0eR1WW20CRYZSPAgg7SZnm+jGl84iqlX/19qNKHvZoT6FC+0t8dbi7nc+5X2n3AdGp44sRVZDssfZXFSLmh5UYKiyqVfh1yD1JdBUrnhiUqnWAtVrryBLdRuOYq0t1GpvG55X54tRxBVpeew0jK9RxGFbrbSHMUAUCp4fB5zJzbSPo67JX3yK25kMq8cg16zqNyrdx2wteoSpN7SNCdPXq5x497qDp5iqwnfI8xH9PzJ/nsqopvgKd2IZ18bn/nqMIdVRf1A+FMnIM0XrjyEjAhM3khhBBC6hOMeQkcTl4IIYSQYMCVl4BhzAshhBBCGhRceSGEEEKCAVdeAiZkJi9p8zbBbnXiivMvUWUfdpet/4dcf5fSbd6QaNWnh6eZ+vn9cgm2bL8kV+mVpRK4NzI9W+l1T3VSemFBT6Uru8i2/HHfHZRxTJEA4TUtpC0AHDXKlHa2lwDjr/e0VvqxFAnYjdolAaNbKiTA1KulJti2T8bRNlICccP3mX8ryhJk2/uIHfuULo9torThkXuTECUBuBantE1x7pX64ZKdPMoqwbCV4eYFQYdFttav9JMGQA/YrTBkHA6XbNFfZkjAbrhD3+pfTwFgTg+gB+zqaQD07f7DrWU+y/U0AHrArlV7UF3plfonBuyW68e0uFBT2gAtdYJXC461mYJsLT7rV7lLfg0Dc/2mB/B7ggBSE9RVfXLaCOUYjEBgzEvg8LERIYQQQhoUnLwQQgghwcCog9dp4tChQxg5ciSio6MRGxuLsWPHoqioqMr6t912Gzp27IiwsDC0bNkSt99+OwoKCkz1LBbLSa/FixfXeHwh89iIEEIIqU/U58dGI0eOxN69e7Fs2TJUVFRgzJgxGD9+PBYtWuSz/p49e7Bnzx488cQT6NKlC3bs2IGbb74Ze/bswVtvvWWqu3DhQgwZMkT9HBsbW+PxcfJCCCGEEMXGjRuxdOlSfPPNN+jbty8A4JlnnsEll1yCJ554AqmpqSe16dq1K/773/+qn9u2bYuHHnoIN9xwAyorK2G3y3QjNjYWycnJtRojHxsRQgghwaCOHhsVFhaaXmVlZagN2dnZiI2NVRMXAMjMzITVasWaNWuq3U9BQQGio6NNExcAmDBhApo0aYJ+/fphwYIFMPw6BPwTMisvljA3LFYXjjwh2+Efmis3rMNfNip94GXZov/Zj2VpCwDuv/Qdpd/sNVDpBza3UXrJWf9SeuRmcdrM/6m/0uF9ZFv9lOd/VvrK6HVKL+8ywHTunyvEoTGo5WalP/iqt9KRZ4sdJ2y3uIq+OSqOJN0VVJkbprQlOlra7je7boqaadvkF2lb+cdWwhcp4XLuojAZU5JDnn96InS3kTiBKiKqcBuFwyeGS9rr6QFcThlfufYLEm6X6ys15Ncgwm7+pTelB7CW+yyPt8pzYD1tgJ4GoFJzITmhl2spAE4w4OjpAXT3kMeUNqA65TVLG1DVMbNzqRrffepwWbvOlsjPgEMjoLGGsHMkZKkjq3SLFi1MxdOmTcP06dMD7jY3NxdNmzY1ldntdsTHxyM3N9dPKzMHDhzAgw8+iPHjx5vKZ86ciYEDByI8PByffvopbr31VhQVFeH222+v0RhDZvJCCCGENEZ27dqFaO3Lp8vl8lnvvvvuw6OPPlplXxs3bqzyeHUoLCzE0KFD0aVLl5MmUQ888IDSvXr1QnFxMR5//HFOXgghhJCGgAV+U5dWuz0AREdHmyYv/rjzzjtx4403VlmnTZs2SE5Oxr59+0zllZWVOHTo0CljVY4cOYIhQ4YgKioK77zzDhwOR5X109PT8eCDD6KsrMzvpMsXnLwQQgghweAM77CbmJiIxMTEU9bLyMhAfn4+cnJy0KdPHwDAihUr4PV6kZ6e7rddYWEhBg8eDJfLhffeew9ut59dRTXWr1+PuLi4Gk1cAE5eCCGEkKBQX63SnTt3xpAhQzBu3DjMmzcPFRUVmDhxIkaMGKGcRrt378agQYPw2muvoV+/figsLMRFF12EkpIS/Otf/1LBw8CxSZPNZsP777+PvLw8nHPOOXC73Vi2bBkefvhh3HXXXVUNxyecvBBCCCHExOuvv46JEydi0KBBsFqtGD58OObMmaOOV1RUYNOmTSgpOZZWZt26dcqJ1K5dO1Nf27ZtQ1paGhwOB+bOnYvJkyfDMAy0a9cOTz75JMaNG1fj8YXM5GXzbS1hdbvRNkvyDl0ycoLSP583X+nLe49Sut0iySEEADdeL7l5nrg0Vmnr51Inuqssf1ns8rwv4usIpQt6i6slqUIcMZ2054MHu5rfno8Luyt9adx3Sq/cJnY2Pf+RJU9yJn2VLx8mi02uKWyvOEa8CVFKOw9IniMAKO0aK/WK5Zg9VjufTRw1zcPFsfVLuOwJ0NQmLqTKSM3JY3IUmZ8CWzVHv0dbhdRdRXCLg6dUy23kdoirqExzykQ6yrT6vh1FgNk95LZIXwWecJ/leg4jh0XLq+Txnduo3KPnQjJ/jTK7h3yX6/hzG/p1DvlxIR07WNNcRacu19/HKp2RdZUniY4fUt+px4kZ4+Pj/W5IBwBpaWkmi/P5559/SsvzkCFDTJvT1YaQmbwQQggh9Q5OmAOCm9QRQgghpEHBlRdCCCEkCNTXgN2GACcvhBBCSDCoxzEv9R0+NiKEEEJIgyJkVl4WXjYPkVFW3LXsVlWW9oK4Ulb2ExvLlhGyU2Gbe8SdBABbK8Rp0+ti2Ub54CTJmfTfv2ibAHWRnEfJX4nLJ+PPW5Te0SzF55hLzzpq+vnjPV2UvqVrjtIx2+U6fqsU7c2XPELf57ZXumWkuHQicmXqXpooeY7CfxZXFQCUJcQqbVSKuyY+Wu6HRdtkqKVL2m+MbCv1bVK/IlKcNuEWcfVU+MlfBACecBmvx5DrsLvF2VOhlUc49XxEcr4Iu+88RZH2UtP5SrxyTeFWcSjtrYhV2qm7ikxuIxmHnsPICd/lthNzG5mOabmK9PxC2v6cXq/v+l6tvj/Hj/WEfT79mgb8ftOrzT6h1SSY3zJr6oBq5ITy44q6hI+NAidkJi+EEEJIvYKPjQKGj40IIYQQ0qDgygshhBASBPjYKHA4eSGEEEKCAR8bBUzITF6SbGWIslnhvmePKvNesFvpv378F6UnDv1E6c8Wytb7AHD7b82UfrndG0rftH6g0jN/GKq0O0O23E9aKFv6j038n9J3dZMg4l8qJBh2YPtfTef+bG1XpRO6S6qBiG2y5f7XJRIc6y2XoNSjv8s4LLESkByxV4JNS5Lk4xCWLX0CQHm8B75oEZ0v7SMk0jbVIekBvJESDB1rlX4qIuWppUNPDyCXdhLeMGlfCdEul1xHqRZtGuWUINtiLTA3wqaVm4JyT0gPoLWJtxZJuSltgJy7XAvYdWrj08utWuxnpR5ke0LQq780AP7L/aQB8JceoKogVO2Pos2iB/nWcCv+ALbub0jfJms81gZ0beQMwMlLwDDmhRBCCCENiqBOXmbNmoWzzz4bUVFRaNq0KYYNG4ZNmzaZ6pSWlmLChAlISEhAZGQkhg8fjry8vCCNmBBCCKkbjse81OYVqgR18rJq1SpMmDABq1evxrJly1BRUYGLLroIxcXFqs7kyZPx/vvv480338SqVauwZ88eXHXVVUEcNSGEEFIHGHXwClGCGvOydOlS08+vvPIKmjZtipycHJx33nkoKCjA/PnzsWjRIgwceCymZOHChejcuTNWr16Nc845JxjDJoQQQkgQqVcxLwUFx3aEjY+PBwDk5OSgoqICmZmZqk6nTp3QsmVLZGdn++yjrKwMhYWFphchhBBS37AYRq1foUq9cRt5vV5MmjQJ5557Lrp2Peaqyc3NhdPpRGxsrKluUlIScnNzffYza9YszJgx46TyIV/cAmuYG1sufFmVXZI+WulOL8hW+llXbVP6pRFDTP04PmyidJPJ4qKBTdwy7hXi7CnIkO3mE58Xh0svp9z6fX3EufJWgbibrk1YYzr32s09lD5qSF/WvfuVXnGos9IWW77S4b/LPNWTGKu0K1ce0R08S8q9xbKNPwA4EuQ6LNq1to44qPSGcHFiJdvlflZEO5WOssh1l0f63ra+8oT0AF5tO324xcFTaoiOcIlLqExzuEQ6yrT6cp+jtTQAegqACC0FAADsr5T30m0RJ5g5DYCWHsDjOz1AuUfumUNb663Qyk/8JqG7iqymNAAWn+V+XUV+XEhVbXnvv42/BiL9pSCo7rn9DyrwMVWrvKpzEHI6oNsoYOrNysuECRPw008/YfHixbXqZ8qUKSgoKFCvXbt21dEICSGEEFIfqBcrLxMnTsQHH3yAL774As2bS4LD5ORklJeXIz8/37T6kpeXh+TkZJ99uVwuuLQEgYQQQkh9hDvsBk5QV14Mw8DEiRPxzjvvYMWKFWjdurXpeJ8+feBwOLB8+XJVtmnTJuzcuRMZGRlneriEEEJI3UG3UcAEdeVlwoQJWLRoEd59911ERUWpOJaYmBiEhYUhJiYGY8eORVZWFuLj4xEdHY3bbrsNGRkZdBoRQgghIUpQJy/PP/88AOD88883lS9cuBA33ngjAOCpp56C1WrF8OHDUVZWhsGDB+O55547wyMlhBBC6hY+NgqcoE5ejGrYvNxuN+bOnYu5c+fW6lwdZhfBbqvAM33bqLJf/youmPZ/+UHplaXiOPjTFZKDCADWjeik9Owbxdnj6dNR6ZQV4v4ZdvMGpde0lbZlxmqlLb3FmfPu9m5K390nx3TuuE3idvmpXHMPHTwk4/td8h+1jRG3S+RuudelKWFKR6z7XemjTWOVNirlXACQFCeWc2uYtE9z7ZAxRXdQOtEmLqbyKHHUuDS3UYWfHEaVEV7TzxWaq8gZrjl+DKkX5RL3ULEh54iy+8lhpOU20vMUhZ/gNioz5TCScx/1SLnJVaS5kGzamm65V+6BTc9tZGi5jSwn5DbSnC82P64ivY3X8O3e0n/NTO4k82024T/vUQ2dS4H8cQ3hP8j1jVD+z/GMQLdRwNSLgF1CCCEk1ODKS+DUG6s0IYQQQkh14MoLIYQQEgz42ChgOHkhhBBCgkQoP/qpDXxsRAghhJAGRcisvBhbd8KwOPDKCxersjcmz1H6notvUfqm7F5KbzxfciEBwKW/iGvkhZUDlXYPkvIWM8W5NDZurdIfZfxR6VWlkjNnZPtvlX7l/UFKR56t5U4CEL5VXEUfH+ku1+YRN46xTSw8lsQE6WuXuHEOdxK3UNjhfKUrm0p+oBNpGy05jPZFRSrdynlA2sfIeOOt8nWiLEbmyA6L3KcKuQUmvOEe08+62yjMLWMs1Ww00U7dVSQusmiH7xxGUVbf5Yl2cyLPEo/05S+3kRMyPt1V5NC+UlVqeYp051ClKbfRCW4jP7mNPH7yDvnLR2R4fX9HMbuWTqgTSF6gGhDQt826zGFUQ2o8Xn6bJtXBMKpIAFbN9iFKyExeCCGEkPoE3UaBw8dGhBBCCGlQcOWFEEIICQZ0GwUMJy+EEEJIELB4j71q0z5U4WMjQgghhDQoQmblJW9ML9hcbiS/sE6Vdb5HXCKH/iq5eJq/LG6aH841T23trVoonfa+HAubsktp4+lopZvZxFKzL0Pqv7RHnEf/bPW20u9tEAfTzsojpnN7d+9VeunuLkrHhO1WOmq71C9LjVHateuw0iXni9vIe1RcNzFNipS2usxOpw4ReXId0ZJ/qZk9X+nyWHHmRFkl9095tO+cO5WRsuZZYVQqbQsXDQBlmpsnyq25igxx6phcRYbmNrIdVfqIV64pyib1d5Q1UdptNed0Oqq5jZwW3VUkvzqm3EYm95BQ4fVdrjuHbCe6jTy+v1t4TS4kPYeRfp/1XEg+uznFkvOpcxX5y59krh9AziO/eZXqiNPdfwMklAM/gwofGwUMV14IIYSQIHDcbVSb1+ni0KFDGDlyJKKjoxEbG4uxY8eiqKioyjbnn38+LBaL6XXzzTeb6uzcuRNDhw5FeHg4mjZtirvvvhuVlZV+evRPyKy8EEIIIfWKerzPy8iRI7F3714sW7YMFRUVGDNmDMaPH49FixZV2W7cuHGYOXOm+jk8PFxpj8eDoUOHIjk5GV9//TX27t2LUaNGweFw4OGHH67R+Dh5IYQQQohi48aNWLp0Kb755hv07dsXAPDMM8/gkksuwRNPPIHU1FS/bcPDw5GcnOzz2Keffoqff/4Zn332GZKSktCzZ088+OCDuPfeezF9+nQ4nU6f7XzBx0aEEEJIEKirx0aFhYWmV1lZWdUnPgXZ2dmIjY1VExcAyMzMhNVqxZo1a6ps+/rrr6NJkybo2rUrpkyZgpKSElO/3bp1Q1JSkiobPHgwCgsLsWHDhhqNMWRWXkaM/QzuSDs++0zejKs3X6X0R31eVPqmayRodlTOGFM/rqESjJu08Dulp8/7n9L3ZEiqgR/Llyqd2fcnpT9b21XptLYS1Bv7U4HSnxR3MJ3bq30I8jZLkGlcU/mgxmyTgNOiFlqw6TrZ3r80OVE6NSTYtF281CmJlqBlAGjn+kXpVbHpSifatC3zYyUo1WWRgF1/aQA8kRIAW6kF5YaFm9MUFHtljHEuPQBXri/WIfem0CsByTF2KS/W0gCkOrQAZq2fCIv53Ee9ch1uizyXLfVIuZ4GQE8P4LRIYGiFFshr08u1+idiCubV2nj9BJx6/aQH0IP69DQARlWBq3UUgNvQAkGZBoCcUeooYLdFixam4mnTpmH69OkBd5ubm4umTZuayux2O+Lj45Gbm+u33fXXX49WrVohNTUVP/zwA+69915s2rQJb7/9tupXn7gAUD9X1a8vQmbyQgghhDRGdu3aheho+WLtcrl81rvvvvvw6KOPVtnXxo0bAx7H+PHjle7WrRtSUlIwaNAgbN26FW3btg24X19w8kIIIYQEgbrKbRQdHW2avPjjzjvvxI033lhlnTZt2iA5ORn79u0zlVdWVuLQoUN+41l8kZ5+bJV+y5YtaNu2LZKTk7F27VpTnby8Y9tw1KRfgJMXQgghJDicYbdRYmIiEhMTT1kvIyMD+fn5yMnJQZ8+fQAAK1asgNfrVROS6rB+/XoAQEpKiur3oYcewr59+9RjqWXLliE6OhpdunTx141PGLBLCCGEEEXnzp0xZMgQjBs3DmvXrsVXX32FiRMnYsSIEcpptHv3bnTq1EmtpGzduhUPPvggcnJysH37drz33nsYNWoUzjvvPHTv3h0AcNFFF6FLly7485//jO+//x6ffPIJ7r//fkyYMMHvoy5/cPJCCCGEBIH6vEnd66+/jk6dOmHQoEG45JJLMGDAALz4ohhbKioqsGnTJuUmcjqd+Oyzz3DRRRehU6dOuPPOOzF8+HC8//77qo3NZsMHH3wAm82GjIwM3HDDDRg1apRpX5jqEjKPjW6N3YboKCuevVecREkvJyjtmSV1rbGyrX78IrPrpvwv++WHV8R5cbZL3Ce7MuW2zs7LVDoreZnS69d1V3rfFbJroWW7bPW/eLc4owDA6ZJnkNGbZd5Z0VKcR2E78qXfPrI8GKvtjBierKUB0Hz1XaIk/cC3MWanU5rjgNJl8TJDjtFcRaWxvtMAlEfLb5gX4hyyRIh7p0RLDxAdJlv3HzsmfcU6fW/3H6e5jY54xG0UZZW+9lbEKh3hEofWUY/uKDoxPYDuKpKxl1Zq6QE0u0CZR8r1bwaV2lb/Du2I7hCynrAlv980AH7aGH7cRv7Kq9wmv67SAPjtPxCnU03LmQaA1HPqcXqA+Pj4KjekS0tLg6H9QWjRogVWrVp1yn5btWqFjz76qNbj48oLIYQQQhoUIbPyQgghhNQn6sptFIpw8kIIIYQEA69x7FWb9iEKJy+EEEJIMKjHMS/1Hca8EEIIIaRBETIrL9duuQj2CBf+N2i2KrtpvDiPLhn+V6Vdw8Vt1PQVyV8EADOf0HIY/VHPYbRC6YF/+EHpz76RHEbzr/xK6YTvJIfRB8WybbKnQMq3bexsOnfnZJlmx/0qrpjCNHHdxP+4TemS5vFKGx7JHdS5qbiWSqIk8VDXsF+VXpPQ23TuVLu4dkoT5GMTbhW3UnksfOKJEpdOmSHjDo8Ux4+ev6hJWLGpfb6WkyjeKcf0HEZxdinXXUh6DqNfS2UHRz2HUbFH+tfzFwGnP4eR7kLS6wNnIIdRVd/a6iqHUT38ZhhQnEA9vI6aEsrxEfUVC2oZ81JnI2l4hMzkhRBCCKlXnOEddhsTfGxECCGEkAYFV14IIYSQIECrdOBw8kIIIYQEA7qNAoaPjQghhBDSoAiZlZfiOc1gd7ix/1lxj1hTkpRu+kK40q77diht+bc506Wew2jHZTL3m77rcqX/2eptpX/8WnIY7bzsiHS0abuSL28boHRMmOQ2ittgnluWtWmqdNjWg0rnpouLJqagUHRzcS5ZXeLA6R2zU+kvm3RTur0zT+nSRKkPAPGaq6g03k8Oo1j5GlCh5SqyRouzR89hFBeh5SMyxI0T75JywOweSnCIq6jAI+9ZrE3a7CiTXE/tXblKF1fqriJxPfnLXwTUXQ4jj0fPUyT3z1/+IuAM5DDyk78ICCCHUV3Vr7KvGp4jRAnlRwkNDYthwFKLoNvatG3ohMzkhRBCCKlXeP//qzbtQxQ+NiKEEEJIg4IrL4QQQkgQ4GOjwOHkhRBCCAkGdBsFTMhMXlxLc2C3OPCnzDtUmf0meWrW6oGvlX71ZdnG/7Jhd5n6eb/kG6Un/nGZ0i++PUTptLGy5X78agkYfelQhtLeEgkwPfidBOLGtZLgw4SfzIGrh86SANXENdJvaet4+KJ30u9K58bHKt097HulP2/aX+lUmwTTliSat7MPs0iwa2mCz9OhMlbal2mBuVFRR5U+oqUBSAovUjrfI0G5SS4JOj52LELpeLveRu5HG6ekPCjStvuPspRq5RJ0HG6VgN2SSil3nxiwqwXm6mkAyvVAXm0r/nItPYAeZOvRgmn1NAB+t/oH4PX4fqprmPqy+iw3N/BzgqoCXWuaBqCG/QQUsFtDQjUNAGlAcIfdgGHMCyGEEEIaFCGz8kIIIYTUJ7jDbuBw8kIIIYQEAz42Chg+NiKEEEJIg4IrL4QQQkgQsHiPvWrTPlQJmclL+eA+8Drc6PjELlU2bNl3Sr/91vlKOyzZSttHiIsFAO789k9Kb/jDAqU/Xi7tl18vjpPK3yTVwKIfzla6U1MZR+I6+QQWdhHnUPRX20znzr+yrdIJmlupbUvZ1t8WLU6nc2M2Kf1m8kA5t/OA0iXJ4syJs4rj52iifydKeZyMt8wQ1447VnP2aG6jppHiEDrolfMlucVVdMgbqXQTh9QHgIMeORZvl/QAG4+mKh3llnMXVsp1RFglNUFRhZTrrqKSSj09gOnUKDUd09xGmqvIprmKKvVyi+428p0GwOsnBQAAGH7+MPl1FcFPudd3Ogd//R87WIfOpbqCaQBMhHK8Q6OBj40Cho+NCCGEENKgCOrk5YsvvsBll12G1NRUWCwWLFmyxHTcMAxMnToVKSkpCAsLQ2ZmJjZv3hycwRJCCCF1iVEHrxAlqJOX4uJi9OjRA3PnzvV5/LHHHsOcOXMwb948rFmzBhERERg8eDBKS0t91ieEEEIaCsfTA9TmFaoENebl4osvxsUXX+zzmGEYmD17Nu6//35cccUVAIDXXnsNSUlJWLJkCUaMGHEmh0oIIYSQekK9jXnZtm0bcnNzkZmZqcpiYmKQnp6O7Oxsv+3KyspQWFhoehFCCCH1juMBu7V5hSj11m2Um3ssd09SUpKpPCkpSR3zxaxZszBjxoyTyt2374E9wgXjSnGrjIvZK+3+Ki6dm7ZdofTrXV419XPdlLuV3ttfcvbY1/6i9AO/DlM6xr1b6divxe1SdlZLqbNeHE3brk9WOvwdcQUBQFR7cSJZXdLX+U1/VfrL5G5K93Z/qvRrzcSxk2oTB01xisxfHRb5OJQ2Nf9SVGjuIUtCmdJFhrh5EqN1V5H0mxquuYq0fETJTinfXyn3P8lRYDr3jrImSqc59yudXyF9RVvlUeKRSt1V5FHa5CrSHhYfNZWbHS3llTbtmFxTRaWfHEYeP64iU7nFZ/mJ+Mth5Nd1o7mHTK6iABxCfi2YNXYh1bC8qnP4oc7yLdVT6CpqxBgw/d4G1D5EqbcrL4EyZcoUFBQUqNeuXbtO3YgQQgg5wzDmJXDq7eQlOfnYCkReXp6pPC8vTx3zhcvlQnR0tOlFCCGEkMZDvZ28tG7dGsnJyVi+fLkqKywsxJo1a5CRkRHEkRFCCCF1gIFaxrwE+wKCR1BjXoqKirBlyxb187Zt27B+/XrEx8ejZcuWmDRpEv7xj3+gffv2aN26NR544AGkpqZi2LBhwRs0IYQQUhdwh92ACerk5dtvv8UFF1ygfs7KygIAjB49Gq+88gruueceFBcXY/z48cjPz8eAAQOwdOlSuN1uf10SQgghpJET1MnL+eefD6OKmaPFYsHMmTMxc+bMWp/rzfafIDrKiq63T1BlD+w7qPSCIS8pPXn2zUo3uUecKAAQ/6ns8Hv3LcOUNsoOK31kRVOlo7uFKZ30tdT5/cI4pVP/J0HFnrPEdWOxiaMFAC5sIa6iDSktlD4vQqzjn7X8g9Jt7HJvjzSXtzrMIvmFSrTwIa8W9u5pIi4iwOwqahInrqJDHjlHi8h8pfd7IpRuGXZI6dzKWKVTnXI/dLdRV/fvpnOvrxRnVqxVcjrlV8i9jdJyGBWWy+Q2XLPNFFc4lXZreYdKK/T8ReYnqWWVct+sphxGUs+Uw8iPe8ifq8jw+HEUoYocRv5cRX7r+y6v0sVyul1FAeQpauyuIhKCeOE3JVm124co9TbmhRBCCGnM1Ge30aFDhzBy5EhER0cjNjYWY8eORVFRkd/627dvh8Vi8fl688035Zp9HF+8eHGNx1dv93khhBBCSHAYOXIk9u7di2XLlqGiogJjxozB+PHjsWjRIp/1W7Rogb1795rKXnzxRTz++OMn7aS/cOFCDBkyRP0cGxtb4/Fx8kIIIYQEg3oasLtx40YsXboU33zzDfr27QsAeOaZZ3DJJZfgiSeeQGpq6kltbDbbSduYvPPOO7jmmmsQGRlpKo+Nja1yy5PqwMdGhBBCSDCop+kBsrOzERsbqyYuAJCZmQmr1Yo1a9ZUq4+cnBysX78eY8eOPenYhAkT0KRJE/Tr1w8LFiyoMvbVH1x5IYQQQhowJ+bwc7lccLlcfmqfmtzcXDRt2tRUZrfbER8fX2V6Hp358+ejc+fO6N+/v6l85syZGDhwIMLDw/Hpp5/i1ltvRVFREW6//fYajTFkJi8v5KfBXWnHw39+TZVNfWGU0vdP/k7p1Le2KX3HyEGmfioPiHPmp4/PUbpl93ylm6+QD9LeP8gOvynPfav00fvOkk4NCRkf0naj0ltSzXmdLon5SOl1bXsp3cUpeX0KWoujJtoqbpzi5tKP7iqqSBWXTpFXchYlJB4xnfuAR9q0jpF7sNsjLqE24ZKLaXeFuKmaO6V+XkWM0h3d8nx0Q0kzOXeEOSjsULk4l0yuogrfrqIiP66iEq1cdxWV+clfBJhzGPlzFVUvh5FvV5FfhxBgcgmdFldRVeeuy1xFNSRUXUXMYRSC1NFjoxYtWpiKp02bhunTp59U/b777sOjjz5aZZcbN26s8nh1OHr0KBYtWoQHHnjgpGN6Wa9evVBcXIzHH3+ckxdCCCGkQVBHVuldu3aZUuH4W3W58847ceONN1bZZZs2bZCcnIx9+/aZyisrK3Ho0KFqxaq89dZbKCkpwahRo05ZNz09HQ8++CDKyspqtFrEyQshhBASBGprdz7etrp5/BITE5GYmHjKehkZGcjPz0dOTg769OkDAFixYgW8Xi/S09NP2X7+/Pm4/PLLq3Wu9evXIy4ursaPuTh5IYQQQoiic+fOGDJkCMaNG4d58+ahoqICEydOxIgRI5TTaPfu3Rg0aBBee+019OvXT7XdsmULvvjiC3z00Ucn9fv+++8jLy8P55xzDtxuN5YtW4aHH34Yd911V43HyMkLIYQQEgzqqVUaAF5//XVMnDgRgwYNgtVqxfDhwzFnzhx1vKKiAps2bUJJSYmp3YIFC9C8eXNcdNFFJ/XpcDgwd+5cTJ48GYZhoF27dnjyyScxbty4Go8vZCYvry+4EDaXG/+75ylV9tKrst3+qGGyYY4nT571rX3LvETW7GwJZG31vp/t/p/+Rumj93RR2nhWPmhXdv5e6Q1asNU18UuUntrR/Ib2dsqH5HB7WWKLs4YrfSRN6uuBueXNJRi3wCsBvk2bFiid5/Eo3SFuv+ncemBuh8g8pXdVJCjd2i1tfq+IV1rf7v+XoylKD4jYpPSBMtkHINYqYwWAQ2VyfVEWGWNBmQTs6oG5xeVyb/TA3NIK+bjrgbnlWrkelAsAlVrArh406zGVa4G5lX4Ccz2nDrK1orrpAeooMLfK9AA1Lfd3jgBSE/ijEQS0MiiXmPAatftQeE/fByo+Pt7vhnQAkJaW5tPi/PDDD+Phhx/22WbIkCGmzelqA/d5IYQQQkiDImRWXgghhJB6RT1+bFTf4eSFEEIICQq13SU3dCcvfGxECCGEkAYFV14IIYSQYMDHRgETMpOXpIXrYbc4MWDQjaostVRcMNsWdFU6clCF0i3/s8vUz9ax4gxqNfVrpcNmtVfa8pzc1r92/Z/Sn7WXJFd/jpM0BRN73KF0b5ec+8BZsp09YN7uv6CDfGgrjEqlva3FkXTAIzqtmWzd/7vmiOneZI/SWys1h1CUlAPAb+WS56KdW9xGO8qaKJ0esUXpdUWtlM6M/Fnp3FLZSCnBKq6nA2V6CgBxSQHA4TK57girLBYe0VxFbou4f46WO5TWXUVluttIq19RIdoO0QDgqfSz3b8/V5HXz2JmTbf6P6GNjsXrs7jmrqKA3EY12w60sbuK/F0fXUWkWngN1OoDfxrdRvUdPjYihBBCSIMiZFZeCCGEkHqF4TUl5g2ofYjCyQshhBASDBjzEjCcvBBCCCHBgDEvAcOYF0IIIYQ0KEJm5cXSpgUsNheSHhWHSt4N3ZRu+sp3Sm96XvIRtR+z29TPeUMOKb17nuTpmdHpPaX/efb1So+OWa704ozBSndzioNmX29xuIRZZHxHuonzCACKtJxE0e0lr9JOj5T3bCHj3VwhOYH6Ndmh9IbyVKV7R0n5z6XNlO7sNl/310UdlL4iJkfplfkdlb4yZp3Se4/GKJ2g5SrKLZEcSVHa1PnwURlruMXs+DlSKvfEpbmBSkrFjaXnMCor9+0qqtTKdeeQR3Mb6c4hAPBWWH0eMzy+5/1GpR9XkZ/cRhZ/OY8QgKvIb30/5VW6jWqWk8ivuyYQp1M9hO4hclrgY6OACZnJCyGEEFKvMFDLyUudjaTBwcdGhBBCCGlQcOWFEEIICQZ8bBQwnLwQQgghwcDrhf+gtOq2D0342IgQQgghDYqQWXn59Y5IWMPcaD9GXEV9ZovjZ/e7sUo/94d/Kf3PP4hzCAD+kfKs0pcOuUvpIWHlSk8eJP0mWCVnz4FzJQeR7hwK6y0Opq2VRTK+jttN5/6+XNw1FzX/RelvSiXf0qCEjUqvOdpW6XMiJe+QP+fQgv1/UHpw8gbTuf+vqL/SzRKKld5+RPIhJaTKt4C9RZLDKMYqbp5DJeIqirTIx6+gxK20+wS30VGTq0iOlZdJDiM9J1FFmW+3kafcj3Oo0v8c3jDlNtLb+HYV+XMP+XUVVeE28nuspu4hf86hKvIqNWb3UFXOIbqKyBmFj40CJmQmL4QQQki9gpOXgOFjI0IIIYQ0KLjyQgghhAQDpgcIGE5eCCGEkCBgGF4YtcgMXZu2DZ2Qmbws/ePziIqy4k8j71Zl85o/p3S3v0xQWg++vfV6CQoFgCir/Hz0skKld1YeUbrD+b8pvbpMgnSv6CnBwp8cTVR6TNtspT8qktQEI5LWmM79YWFPpYfGfK/0/x2QYNrbmko6gpm/Xybnbvmj0s/vOl/prCb/U/qXw0lKJzczz+i35Utgbnxr+djkFcp2/zHavTlUpG/3L+VFxRKY69ICdsuOOrRy8z0v146ZAnDLfG/rb5RLuSnItsJ38C3K/ZQDsFT6CXb1F+Rb08Dcqv72+Amo9Rdo67/cT/9Vpgeo4liQqGlqAgbfknqPYdRu9YQxL4QQQgghDYOQWXkhhBBC6hVGLWNeQnjlhZMXQgghJBh4vVU8160GIRzzwsdGhBBCCGlQcOWFEEIICQZ8bBQwITN5yfO4UOyxos+kdarsw5JIpceM+FTpeQXNlH544Fumfh450Fvp2d3fkPK8TKVntHpX6Vm/D5W+Wkr5xK3XKP1yO+lnxM+jlF5ylqQpAIBZmy5W+r6e4kS6dXea0k83k8W0n3KTlW7e2qX0tn0JSid0FPdP3gHZ0j/aIvUBIP+QpDmItMqxkkJJhaC7hMoLXT7LK4t055B8/DwlUn6i4wdHfbuHUOqnvMz3gqKl3M9CY0UVW/RX+Gnj14VUs/QAftMGIACX0Bn4O1ZXjh9u0U8IYHi9MGrx2CiUrdJ8bEQIIYSQBkXIrLwQQggh9Qo+NgoYTl4IIYSQYOA1avecNIQnL3xsRAghhJAGBVdeCCGEkGBgGKg6R0h12ocmITN5GfPhX2F1u7HlmhdUWbv//FXp6pQDQLuPxCU045oNSv/1q55KP3eNOIFyvm2ndNs24m7a/H0LpZt1lPxAezc2VTqhmzh8ACB/S5zS0b3F5XN0h7QPSxeXT8Xv0l53/Hhzpa3u+MEBl+9yANbDvt1A1ny77/Ij4gTSsRX7KS/xvwhoLfV9zFrm26ljLfdT7sdVZPXjEAIAq6dm5f6MA37LA3Dd0PFDSOPA8BowavHLZnDyQgghhJAziuFF7VZeaJWu18ydOxdpaWlwu91IT0/H2rVrgz0kQgghpNHy0EMPoX///ggPD0dsbGy12hiGgalTpyIlJQVhYWHIzMzE5s2bTXUOHTqEkSNHIjo6GrGxsRg7diyKiopqPL56P3l54403kJWVhWnTpmHdunXo0aMHBg8ejH379gV7aIQQQkjAGF6j1q/TRXl5Oa6++mrccsst1W7z2GOPYc6cOZg3bx7WrFmDiIgIDB48GKWlparOyJEjsWHDBixbtgwffPABvvjiC4wfP77G46v3k5cnn3wS48aNw5gxY9ClSxfMmzcP4eHhWLBgQbCHRgghhASO4a396zQxY8YMTJ48Gd26davepRgGZs+ejfvvvx9XXHEFunfvjtdeew179uzBkiVLAAAbN27E0qVL8fLLLyM9PR0DBgzAM888g8WLF2PPnj01Gl+9jnkpLy9HTk4OpkyZosqsVisyMzORnZ3ts01ZWRnKysrUzwUFBQAA7/+f+RUekUhLrzYbrE55IG3qqpzn5rl5bp6b5z795y4sOjYhOBPBsJWoqNUedZWoAAAUFhaayl0uF1wul68mp41t27YhNzcXmZmSKicmJgbp6enIzs7GiBEjkJ2djdjYWPTt21fVyczMhNVqxZo1a3DllVdW/4RGPWb37t0GAOPrr782ld99991Gv379fLaZNm3a8S0L+eKLL7744iug165du07b/21Hjx41kpOT62SckZGRJ5VNmzatzsa6cOFCIyYm5pT1vvrqKwOAsWfPHlP51VdfbVxzzTWGYRjGQw89ZHTo0OGktomJicZzzz1Xo3HV65WXQJgyZQqysrLUz/n5+WjVqhV27tyJmJiYII7szFJYWIgWLVpg165diI6OPnWDRgKvm9cdCvC6T991G4aBI0eOIDU19bT0DwButxvbtm1DeXl5rfsyDAMWi3m7B3+rLvfddx8effTRKvvbuHEjOnXqVOtxnW7q9eSlSZMmsNlsyMvLM5Xn5eUhOTnZZxt/y2UxMTEh9Ut+nOjoaF53CMHrDi143aeHM/FF1+12w+12n/bz6Nx555248cYbq6zTpk2bgPo+/n9yXl4eUlJSVHleXh569uyp6pxotqmsrMShQ4f8/p/uj3o9eXE6nejTpw+WL1+OYcOGAQC8Xi+WL1+OiRMnBndwhBBCSAMiMTERiYmJp6Xv1q1bIzk5GcuXL1eTlcLCQqxZs0Y5ljIyMpCfn4+cnBz06dMHALBixQp4vV6kp6fX6Hz13m2UlZWFl156Ca+++io2btyIW265BcXFxRgzZkywh0YIIYQ0Snbu3In169dj586d8Hg8WL9+PdavX2/ak6VTp0545513AAAWiwWTJk3CP/7xD7z33nv48ccfMWrUKKSmpqrFh86dO2PIkCEYN24c1q5di6+++goTJ07EiBEjavyYrl6vvADAtddei/3792Pq1KnIzc1Fz549sXTpUiQlJVWrvcvlwrRp08545HWw4XXzukMBXjevm5wepk6dildffVX93KtXLwDA559/jvPPPx8AsGnTJuXoBYB77rkHxcXFGD9+PPLz8zFgwAAsXbrU9Hjs9ddfx8SJEzFo0CBYrVYMHz4cc+bMqfH4LIYRwskRCCGEENLgqPePjQghhBBCdDh5IYQQQkiDgpMXQgghhDQoOHkhhBBCSIOiUU9e5s6di7S0NLjdbqSnp2Pt2rXBHlKdMmvWLJx99tmIiopC06ZNMWzYMGzatMlUp7S0FBMmTEBCQgIiIyMxfPjwkzb9a+g88sgjyqZ3nMZ63bt378YNN9yAhIQEhIWFoVu3bvj222/VcaMaKekbGh6PBw888ABat26NsLAwtG3bFg8++KAp90xjuO4vvvgCl112GVJTU2GxWFQyu+NU5xoPHTqEkSNHIjo6GrGxsRg7dqzJ2lofqeq6KyoqcO+996Jbt26IiIhAamoqRo0adVISv4Z43aR2NNrJyxtvvIGsrCxMmzYN69atQ48ePTB48OCTdvdryKxatQoTJkzA6tWrsWzZMlRUVOCiiy5CcXGxqjN58mS8//77ePPNN7Fq1Srs2bMHV111VRBHXbd88803eOGFF9C9e3dTeWO87sOHD+Pcc8+Fw+HAxx9/jJ9//hn//Oc/ERcXp+pUJyV9Q+PRRx/F888/j2effRYbN27Eo48+isceewzPPPOMqtMYrru4uBg9evTA3LlzfR6vzjWOHDkSGzZswLJly/DBBx/giy++wPjx48/UJQREVdddUlKCdevW4YEHHsC6devw9ttvY9OmTbj88stN9RridZNaUqNMSA2Ifv36GRMmTFA/ezweIzU11Zg1a1YQR3V62bdvnwHAWLVqlWEYhpGfn284HA7jzTffVHU2btxoADCys7ODNcw648iRI0b79u2NZcuWGX/84x+NO+64wzCMxnvd9957rzFgwAC/x71er5GcnGw8/vjjqiw/P99wuVzGv//97zMxxNPC0KFDjb/85S+msquuusoYOXKkYRiN87oBGO+88476uTrX+PPPPxsAjG+++UbV+fjjjw2LxWLs3r37jI29Npx43b5Yu3atAcDYsWOHYRiN47pJzWmUKy/l5eXIyckxpea2Wq3IzMxEdnZ2EEd2ejm+WVB8fDwAICcnBxUVFab70KlTJ7Rs2bJR3IcJEyZg6NChpusDGu91v/fee+jbty+uvvpqNG3aFL169cJLL72kjp8qJX1DpX///li+fDl+/fVXAMD333+PL7/8EhdffDGAxnvdOtW5xuzsbMTGxqJv376qTmZmJqxWK9asWXPGx3y6KCgogMViQWxsLIDQuW5ipt7vsBsIBw4cgMfjOWkX3qSkJPzyyy9BGtXpxev1YtKkSTj33HPRtWtXAEBubi6cTqf6JT9OUlIScnNzgzDKumPx4sVYt24dvvnmm5OONdbr/u233/D8888jKysLf/vb3/DNN9/g9ttvh9PpxOjRo9W1+frcN+Trvu+++1BYWIhOnTrBZrPB4/HgoYcewsiRIwGg0V63TnWuMTc3F02bNjUdt9vtiI+PbzT3obS0FPfeey+uu+46lZgxFK6bnEyjnLyEIhMmTMBPP/2EL7/8MthDOe3s2rULd9xxB5YtW3bGs7IGE6/Xi759++Lhhx8GcGy77p9++gnz5s3D6NGjgzy608d//vMfvP7661i0aBHOOussrF+/HpMmTUJqamqjvm5ipqKiAtdccw0Mw8Dzzz8f7OGQINMoHxs1adIENpvtJHdJXl5ejdNuNwQmTpyIDz74AJ9//jmaN2+uypOTk1FeXo78/HxT/YZ+H3JycrBv3z707t0bdrsddrsdq1atwpw5c2C325GUlNQorzslJQVdunQxlXXu3Bk7d+4EYE5Jr9PQr/vuu+/GfffdhxEjRqBbt27485//jMmTJ2PWrFkAGu9161TnGpOTk08yJFRWVuLQoUMN/j4cn7js2LEDy5YtU6suQOO+buKfRjl5cTqd6NOnD5YvX67KvF4vli9fjoyMjCCOrG4xDAMTJ07EO++8gxUrVqB169am43369IHD4TDdh02bNmHnzp0N+j4MGjQIP/74o8pyun79evTt2xcjR45UujFe97nnnnuSFf7XX39Fq1atAJhT0h/neEr6hnzdJSUlsFrNf6psNhu8Xi+AxnvdOtW5xoyMDOTn5yMnJ0fVWbFiBbxeL9LT08/4mOuK4xOXzZs347PPPkNCQoLpeGO9bnIKgh0xfLpYvHix4XK5jFdeecX4+eefjfHjxxuxsbFGbm5usIdWZ9xyyy1GTEyMsXLlSmPv3r3qVVJSourcfPPNRsuWLY0VK1YY3377rZGRkWFkZGQEcdSnB91tZBiN87rXrl1r2O1246GHHjI2b95svP7660Z4eLjxr3/9S9V55JFHjNjYWOPdd981fvjhB+OKK64wWrdubRw9ejSII68do0ePNpo1a2Z88MEHxrZt24y3337baNKkiXHPPfeoOo3huo8cOWJ89913xnfffWcAMJ588knju+++U66a6lzjkCFDjF69ehlr1qwxvvzyS6N9+/bGddddF6xLqhZVXXd5eblx+eWXG82bNzfWr19v+jtXVlam+miI101qR6OdvBiGYTzzzDNGy5YtDafTafTr189YvXp1sIdUpwDw+Vq4cKGqc/ToUePWW2814uLijPDwcOPKK6809u7dG7xBnyZOnLw01ut+//33ja5duxoul8vo1KmT8eKLL5qOe71e44EHHjCSkpIMl8tlDBo0yNi0aVOQRls3FBYWGnfccYfRsmVLw+12G23atDH+/ve/m/7zagzX/fnnn/v8fR49erRhGNW7xoMHDxrXXXedERkZaURHRxtjxowxjhw5EoSrqT5VXfe2bdv8/p37/PPPVR8N8bpJ7bAYhrZNJSGEEEJIPadRxrwQQgghpPHCyQshhBBCGhScvBBCCCGkQcHJCyGEEEIaFJy8EEIIIaRBwckLIYQQQhoUnLwQQgghpEHByQshpFps374dFosF69evD/ZQCCEhDicvhDQQbrzxRlgsFlgsFjgcDiQlJeHCCy/EggULVJ6fujzXsGHD6rRPQgipKzh5IaQBMWTIEOzduxfbt2/Hxx9/jAsuuAB33HEHLr30UlRWVgZ7eIQQckbg5IWQBoTL5UJycjKaNWuG3r17429/+xveffddfPzxx3jllVcAAPn5+bjpppuQmJiI6OhoDBw4EN9//73qY/r06ejZsydeeOEFtGjRAuHh4bjmmmtQUFCgjr/66qt499131UrPypUrVfvffvsNF1xwAcLDw9GjRw9kZ2efyVtACCGcvBDS0Bk4cCB69OiBt99+GwBw9dVXY9++ffj444+Rk5OD3r17Y9CgQTh06JBqs2XLFvznP//B+++/j6VLl+K7777DrbfeCgC46667cM0116hVnr1796J///6q7d///nfcddddWL9+PTp06IDrrruOqz6EkDMKJy+ENAI6deqE7du348svv8TatWvx5ptvom/fvmjfvj2eeOIJxMbG4q233lL1S0tL8dprr6Fnz54477zz8Mwzz2Dx4sXIzc1FZGQkwsLC1CpPcnIynE6nanvXXXdh6NCh6NChA2bMmIEdO3Zgy5YtwbhsQkiIwskLIY0AwzBgsVjw/fffo6ioCAkJCYiMjFSvbdu2YevWrap+y5Yt0axZM/VzRkYGvF4vNm3adMpzde/eXemUlBQAwL59++rwagghpGrswR4AIaT2bNy4Ea1bt0ZRURFSUlJMMSrHiY2NrZNzORwOpS0WCwDUuduJEEKqgpMXQho4K1aswI8//ojJkyejefPmyM3Nhd1uR1pamt82O3fuxJ49e5CamgoAWL16NaxWKzp27AgAcDqd8Hg8Z2L4hBBSYzh5IaQBUVZWhtzcXHg8HuTl5WHp0qWYNWsWLr30UowaNQpWqxUZGRkYNmwYHnvsMXTo0AF79uzBhx9+iCuvvBJ9+/YFALjdbowePRpPPPEECgsLcfvtt+Oaa65BcnIyACAtLQ2ffPIJNm3ahISEBMTExATzsgkhxAQnL4Q0IJYuXYqUlBTY7XbExcWhR48emDNnDkaPHg2r9VgI20cffYS///3vGDNmDPbv34/k5GScd955SEpKUv20a9cOV111FS655BIcOnQIl156KZ577jl1fNy4cVi5ciX69u2LoqIifP7551Wu5BBCyJnEYhiGEexBEELOHNOnT8eSJUu4zT8hpMFCtxEhhBBCGhScvBBCCCGkQcHHRoQQQghpUHDlhRBCCCENCk5eCCGEENKg4OSFEEIIIQ0KTl4IIYQQ0qDg5IUQQgghDQpOXgghhBDSoODkhRBCCCENCk5eCCGEENKg4OSFEEIIIQ2K/wcnumZWHU5nbgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:45.973429Z",
     "iopub.status.busy": "2025-02-07T13:04:45.972950Z",
     "iopub.status.idle": "2025-02-07T13:04:46.154666Z",
     "shell.execute_reply": "2025-02-07T13:04:46.153963Z",
     "shell.execute_reply.started": "2025-02-07T13:04:45.973393Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.155822Z",
     "iopub.status.busy": "2025-02-07T13:04:46.155466Z",
     "iopub.status.idle": "2025-02-07T13:04:46.162432Z",
     "shell.execute_reply": "2025-02-07T13:04:46.161719Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.155801Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.163517Z",
     "iopub.status.busy": "2025-02-07T13:04:46.163224Z",
     "iopub.status.idle": "2025-02-07T13:04:46.170126Z",
     "shell.execute_reply": "2025-02-07T13:04:46.169496Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.163493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2750, 0.1794, 0.5456],\n",
      "        [0.6274, 0.1746, 0.1980]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.171277Z",
     "iopub.status.busy": "2025-02-07T13:04:46.170813Z",
     "iopub.status.idle": "2025-02-07T13:04:46.408667Z",
     "shell.execute_reply": "2025-02-07T13:04:46.407767Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.171256Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.410056Z",
     "iopub.status.busy": "2025-02-07T13:04:46.409687Z",
     "iopub.status.idle": "2025-02-07T13:04:46.647812Z",
     "shell.execute_reply": "2025-02-07T13:04:46.647129Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.410028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.648900Z",
     "iopub.status.busy": "2025-02-07T13:04:46.648590Z",
     "iopub.status.idle": "2025-02-07T13:04:46.659361Z",
     "shell.execute_reply": "2025-02-07T13:04:46.658607Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.648880Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.660464Z",
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     "shell.execute_reply": "2025-02-07T13:04:46.665979Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.660443Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.667683Z",
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     "shell.execute_reply.started": "2025-02-07T13:04:46.667662Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
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     "shell.execute_reply": "2025-02-07T13:04:46.682910Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.677775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2025-02-06T14:04:30.513830Z"
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    "execution": {
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     "shell.execute_reply": "2025-02-07T13:04:46.830900Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.684446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:46.833368Z",
     "iopub.status.busy": "2025-02-07T13:04:46.832913Z",
     "iopub.status.idle": "2025-02-07T13:04:47.312407Z",
     "shell.execute_reply": "2025-02-07T13:04:47.311680Z",
     "shell.execute_reply.started": "2025-02-07T13:04:46.833329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.313628Z",
     "iopub.status.busy": "2025-02-07T13:04:47.313366Z",
     "iopub.status.idle": "2025-02-07T13:04:47.331981Z",
     "shell.execute_reply": "2025-02-07T13:04:47.331281Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.313606Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.333310Z",
     "iopub.status.busy": "2025-02-07T13:04:47.332776Z",
     "iopub.status.idle": "2025-02-07T13:04:47.338468Z",
     "shell.execute_reply": "2025-02-07T13:04:47.337849Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.333281Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.339808Z",
     "iopub.status.busy": "2025-02-07T13:04:47.339311Z",
     "iopub.status.idle": "2025-02-07T13:04:47.344285Z",
     "shell.execute_reply": "2025-02-07T13:04:47.343600Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.339782Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.345595Z",
     "iopub.status.busy": "2025-02-07T13:04:47.345311Z",
     "iopub.status.idle": "2025-02-07T13:04:47.467031Z",
     "shell.execute_reply": "2025-02-07T13:04:47.466505Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.345561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.467901Z",
     "iopub.status.busy": "2025-02-07T13:04:47.467704Z",
     "iopub.status.idle": "2025-02-07T13:04:47.472333Z",
     "shell.execute_reply": "2025-02-07T13:04:47.471619Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.467882Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.473784Z",
     "iopub.status.busy": "2025-02-07T13:04:47.473278Z",
     "iopub.status.idle": "2025-02-07T13:04:47.479536Z",
     "shell.execute_reply": "2025-02-07T13:04:47.478857Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.473746Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"dropout_20.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.481008Z",
     "iopub.status.busy": "2025-02-07T13:04:47.480484Z",
     "iopub.status.idle": "2025-02-07T13:04:47.485524Z",
     "shell.execute_reply": "2025-02-07T13:04:47.484850Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.480974Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.486687Z",
     "iopub.status.busy": "2025-02-07T13:04:47.486372Z",
     "iopub.status.idle": "2025-02-07T13:04:47.490707Z",
     "shell.execute_reply": "2025-02-07T13:04:47.490197Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.486668Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.491708Z",
     "iopub.status.busy": "2025-02-07T13:04:47.491336Z",
     "iopub.status.idle": "2025-02-07T13:04:47.499343Z",
     "shell.execute_reply": "2025-02-07T13:04:47.498779Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.491688Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.500436Z",
     "iopub.status.busy": "2025-02-07T13:04:47.500018Z",
     "iopub.status.idle": "2025-02-07T13:04:47.778406Z",
     "shell.execute_reply": "2025-02-07T13:04:47.777730Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.500415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.2, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:47.779454Z",
     "iopub.status.busy": "2025-02-07T13:04:47.779239Z",
     "iopub.status.idle": "2025-02-07T13:04:48.627746Z",
     "shell.execute_reply": "2025-02-07T13:04:48.627193Z",
     "shell.execute_reply.started": "2025-02-07T13:04:47.779432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T13:04:48.628695Z",
     "iopub.status.busy": "2025-02-07T13:04:48.628422Z",
     "iopub.status.idle": "2025-02-07T13:48:36.820277Z",
     "shell.execute_reply": "2025-02-07T13:48:36.819594Z",
     "shell.execute_reply.started": "2025-02-07T13:04:48.628675Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "54499it [43:46, 20.75it/s, epoch=51, loss=3.4, val_loss=3.46]                         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 52 / global_step 54500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:48:36.822118Z",
     "iopub.status.busy": "2025-02-07T13:48:36.821631Z",
     "iopub.status.idle": "2025-02-07T13:48:37.037420Z",
     "shell.execute_reply": "2025-02-07T13:48:37.036753Z",
     "shell.execute_reply.started": "2025-02-07T13:48:36.822092Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/dropout_20.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:48:37.038514Z",
     "iopub.status.busy": "2025-02-07T13:48:37.038194Z",
     "iopub.status.idle": "2025-02-07T13:48:52.536495Z",
     "shell.execute_reply": "2025-02-07T13:48:52.535783Z",
     "shell.execute_reply.started": "2025-02-07T13:48:37.038490Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "7it [00:00, 68.84it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "15it [00:00, 70.52it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:14, 67.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.332451060938786\n",
      "testing bleu: 0.5886035109181292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:48:52.538065Z",
     "iopub.status.busy": "2025-02-07T13:48:52.537406Z",
     "iopub.status.idle": "2025-02-07T13:48:52.540881Z",
     "shell.execute_reply": "2025-02-07T13:48:52.540341Z",
     "shell.execute_reply.started": "2025-02-07T13:48:52.538027Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:48:52.541875Z",
     "iopub.status.busy": "2025-02-07T13:48:52.541569Z",
     "iopub.status.idle": "2025-02-07T13:48:53.017098Z",
     "shell.execute_reply": "2025-02-07T13:48:53.016488Z",
     "shell.execute_reply.started": "2025-02-07T13:48:52.541854Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:48:53.017939Z",
     "iopub.status.busy": "2025-02-07T13:48:53.017751Z",
     "iopub.status.idle": "2025-02-07T13:48:54.279757Z",
     "shell.execute_reply": "2025-02-07T13:48:54.279163Z",
     "shell.execute_reply.started": "2025-02-07T13:48:53.017920Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  7%|▋         | 9/128 [00:00<00:01, 61.89it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a yellow boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
